System

Abstract

A system is provided.SOLUTION: A system comprising: means for recording a user's voice; means for transmitting the recorded voice to a server; means for analyzing voice data and generating a voice profile in the server; means for performing transcription based on the voice profile; means for setting parameters of synthesized voice; means for assigning a unique identifier to the voice data; means for generating synthesized voice; and means for allowing the user to download the generated voice data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's society, voice problems (losing voice, feeling unwell, absence, etc.) can occur during presentations or important events. This can make it difficult to deliver the presentation as planned. There are also needs for bereaved families to utilize the voices of deceased individuals, or for individuals to monetize their own voices, but existing technology is unable to adequately resolve these issues. Therefore, the objective of this invention is to provide a system that allows users' voices to be input into AI, enabling various uses such as voice generation, reading aloud, and even selling the voice data. [Means for solving the problem]

[0005] The present invention provides a system including a means for recording a user's voice, a means for transmitting the recorded voice to a server, a means for analyzing the voice data on the server and generating a voice profile, a means for transcribing the voice data based on the voice profile, a means for setting parameters for synthetic voice, a means for assigning a unique identifier to the voice data, a means for generating synthetic voice, and a means for the user to download the generated voice data. This system allows users to have appropriate reading services performed even if voice problems arise, and also enables monetization of voice data while preventing unauthorized use.

[0006] "User" refers to an individual or organization that uses this system, and is the entity that records their own voice or uses the generated voice data.

[0007] "Audio recording means" refers to the functionality of a device or application that allows a user to record their own voice.

[0008] "Server" means a remote computer system that receives, analyzes, processes, and stores voice data sent by a user.

[0009] "Audio Data" refers to digital data of audio recorded and transmitted by the User.

[0010] An "audio profile" is a data set with individual characteristics generated from audio data analyzed by the server.

[0011] "Transcription" is the process of converting audio data into text form.

[0012] "Synthetic speech parameters" refers to setting information such as sound quality, tone, and speed used when generating synthetic speech.

[0013] A "unique identifier" is a unique identification or code that verifies the specificity of the generated audio data.

[0014] "Synthetic voice" is artificial voice data generated by the server based on the voice profile and synthetic voice parameters.

[0015] "Means for downloading" refers to the process or function by which a user transfers and saves data stored on a server to their own device. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. The program processing of this system is explained in natural language below, and is described in detail with specific examples.

[0038] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and starts recording by tapping the "Start Recording" button. The user speaks their message and taps the "Stop Recording" button when recording is finished. The recorded voice data is temporarily stored in the device's local storage.

[0039] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request. The server receives this request and obtains the audio data.

[0040] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[0041] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[0042] To set the parameters of the synthesized voice, the server sets the necessary parameters based on the voice profile, including sound quality, tone, speed, etc. The server then assigns a unique identifier (e.g., NFT) to the voice data to prevent unauthorized use or forgery of the data.

[0043] In generating synthetic speech, the server uses the set parameters to activate the speech synthesis engine and generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The server stores the generated synthetic speech in a database.

[0044] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[0045] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[0046] In this way, the present invention provides a system that can effectively utilize user voices, solve various voice problems, and monetize voice data.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[0050] Step 2:

[0051] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0052] Step 3:

[0053] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[0054] Step 4:

[0055] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[0056] Step 5:

[0057] The server sets the parameters of the synthesized voice. The server uses a voice profile to set the parameters required for the synthesized voice (quality, tone, speed, etc.). This setting allows the synthesized voice to be created to reflect the user's individual voice characteristics.

[0058] Step 6:

[0059] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[0060] Step 7:

[0061] The server generates synthetic speech. The server starts a speech synthesis engine based on the set parameters and generates synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics is generated. The generated speech data is stored in a database.

[0062] Step 8:

[0063] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[0064] Step 9:

[0065] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[0066] Example 1

[0067] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0068] Conventional systems that use voice data require a great deal of manual work and specialized knowledge for users to effectively utilize voice data. For example, each step, such as recording, analyzing, transcribing, and generating synthesized voice data, must be performed manually, which poses a significant time and labor cost. Furthermore, there are insufficient measures in place to prevent unauthorized use or forgery of voice data, which poses security challenges. Therefore, there is a need for a system that allows users to easily collect, analyze, synthesize, and utilize voice data.

[0069] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0070] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to an information processing device, means for analyzing the voice data in the information processing device and generating a voice profile, means for transcribing based on the voice profile, means for setting parameters for the synthetic voice, means for assigning an identification code to the voice data, means for generating the synthetic voice, and means for the user to download the generated voice data. This allows users to easily record, analyze, and transcribe their own voice data and use it as synthetic voice, while also ensuring security to prevent fraudulent use and counterfeiting.

[0071] "User" refers to an individual who uses this system.

[0072] "Voice" refers to human speech and other sounds that travel as vibrations through the air (sound waves).

[0073] "Recording" refers to the act of recording sound as digital data using an electronic device.

[0074] "Information processing device" is a general term for electronic devices that perform processes such as saving, analyzing, and transmitting data, and here it mainly refers to servers.

[0075] "Audio data" refers to data that represents recorded audio in digital form.

[0076] An "audio profile" refers to a data set that extracts individual features and characteristics of audio data.

[0077] "Transcription" refers to the conversion of audio data into text format.

[0078] "Synthetic speech" refers to speech that is artificially generated based on text data.

[0079] "Parameters" refer to variables that set audio characteristics, such as quality, tone, and speed.

[0080] The "identification code" refers to a code that is assigned to audio data as unique identification information.

[0081] A "voice recognition engine" refers to software or hardware that analyzes voice data and converts it into text data.

[0082] "Identification code generation algorithm" refers to a calculation method for generating a unique identification code for audio data.

[0083] "Downloading" refers to transferring and storing audio data from an information processing device to another electronic device.

[0084] The present invention provides a system that efficiently acquires, analyzes, and generates voice data, allowing users to effectively utilize the data. A specific method for implementing this system will be described below.

[0085] The user uses the device to record their own voice. The user launches an application on their smartphone or computer (e.g., the "Voice Recorder" app) and taps the "Start Recording" button to begin recording. When recording is finished, the user taps the "Stop Recording" button. The recorded voice data is temporarily stored in the device's local storage.

[0086] To send the recorded audio data to the server, the device generates an HTTP request that includes the audio file. The server receives this request and uses the HTTP protocol to retrieve the audio data. Specifically, the device includes all audio data meta information in the request header and attaches the audio file to the request body.

[0087] When the server receives the voice data, it calls a speech recognition engine (e.g., "Google® Cloud Speech-to-Text API") to analyze the voice data. The engine identifies the phonemes and words in the voice data and converts it into text format. It then generates a voice profile, which contains the individual characteristics of the voice data.

[0088] Based on the voice profile, the server converts the voice data into text format, and the generated text is stored in the server's database. It also sets voice synthesis parameters (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data to prevent fraud and counterfeiting.

[0089] Next, the server invokes a speech synthesis engine (e.g., "Amazon Polly") using the set parameters to generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[0090] To use the generated voice data, the user must tap the "Download generated voice" button in the application. The device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then stores the received voice data locally and makes it playable.

[0091] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[0092] An example of a prompt sentence to input to the generative AI model is as follows:

[0093] "Hello, my name is Taro Tanaka. I'd like to move on to the next slide in my presentation."

[0094] In this way, the present invention provides a system that makes effective use of the user's voice, solves various voice problems, and further monetizes voice data.

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1:

[0097] The user launches an application on the device and taps the "Start Recording" button, which activates the device's built-in microphone and captures the user's voice as digital data.

[0098] Input: User utterance

[0099] Output: Digital audio data (e.g., "Data being recorded")

[0100] Step 2:

[0101] When the user has finished recording, they tap the "Stop Recording" button, which saves the digital audio data to the device's local storage.

[0102] Input: Data being recorded

[0103] Output: Audio file saved in the device's local storage (e.g., WAV format file)

[0104] Step 3:

[0105] The device generates an HTTP request to send the recorded audio data to the server, attaches the audio file to this request, and sends it to the server.

[0106] Input: Audio files stored in the device's local storage

[0107] Output: HTTP request sent to the server

[0108] Step 4:

[0109] The server receives the HTTP request, retrieves the provided voice data, and passes the received voice data to the speech recognition engine.

[0110] Input: HTTP request sent from the terminal

[0111] Output: Speech data fed to the speech recognition engine

[0112] Step 5:

[0113] The server uses a speech recognition engine (e.g., a "Speech Recognition Service API") to analyze the audio data and generate a speech profile, which is a data set containing the features of the audio data.

[0114] Input: Audio data

[0115] Output: Audio Profile

[0116] Step 6:

[0117] The server converts the voice data into text based on the voice profile, and the generated text data is stored in the server's database.

[0118] Input: Audio Profile

[0119] Output: Text data stored in the database

[0120] Step 7:

[0121] The server sets the parameters of the synthesized voice (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data. The identification code is important to prevent fraud and forgery.

[0122] Input: Audio Profile

[0123] Output: Audio data with identification code

[0124] Step 8:

[0125] The server invokes a speech synthesis engine (e.g., a "speech synthesis service") using the configured parameters to generate a synthesized voice. This voice file is stored in the server's database.

[0126] Input: Audio data with parameters set

[0127] Output: Synthesized speech files stored in a database

[0128] Step 9:

[0129] The user taps the "Download generated audio" button, which causes the device to send an HTTP request to the server to retrieve the specific audio data.

[0130] Input: User download request

[0131] Output: HTTP request to the server

[0132] Step 10:

[0133] The server receives the HTTP request and returns the generated synthesized voice data to the terminal.

[0134] Input: HTTP request

[0135] Output: Synthesized voice data sent to the device

[0136] Step 11:

[0137] The device saves the received synthesized voice data locally and prepares it for playback. Specifically, it saves the voice file and prepares it for playback on a media player.

[0138] Input: Synthesized voice data sent from the server

[0139] Output: Locally saved playable audio file

[0140] Through the above steps, the present invention enables efficient recording, analysis, transcription, and reuse of user speech as synthesized speech.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] Conventional voice authentication systems have issues with voice recognition accuracy and security, and are not sufficiently effective in fields requiring advanced authentication. Furthermore, there is a lack of means to prevent unauthorized use of voices that exploit the voice characteristics of individual users. The object of this invention is to provide a highly accurate and secure authentication system that utilizes user voice data, thereby improving the reliability and convenience of voice authentication.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0145] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data and generating a voice profile in the server, means for transcribing based on the voice profile, means for setting parameters for synthetic voice, means for assigning a unique identifier to the voice data, means for generating synthetic voice, means for the user to download the generated voice data, means for performing security authentication using the user's voice, means for storing a voice profile of phrases required for security authentication, and means for performing access control based on the results of voice authentication, thereby enabling highly accurate and secure voice authentication.

[0146] "User voice" refers to the user's vocalizations that are recorded and analyzed by the system.

[0147] A "means for recording" is a function that uses a device such as a microphone to capture the user's voice in digital form.

[0148] "Means of transmission" refers to the technology for transferring recorded data to a server via the Internet or a network.

[0149] The "means for analyzing and generating a voice profile" is a function that uses a voice recognition engine to analyze voice data and compile voice characteristics into a data set.

[0150] "Transcribing means" refers to a technology that converts audio data into text format based on an audio profile.

[0151] The "means for setting parameters of synthesized speech" is a function for setting speech characteristics such as sound quality, tone, and speed.

[0152] "Means for assigning an identifier" refers to a technology for assigning a unique identification code to audio data.

[0153] "Means for generating synthetic voice" refers to technology that generates artificial voice that reproduces the user's voice characteristics based on set parameters.

[0154] The "means for downloading" is a function for importing the generated audio data into the user's device.

[0155] "Means for performing security authentication" refers to technology that uses the user's voice to perform identity authentication.

[0156] A "means for storing voice profiles" is a technology that stores a data set of voice characteristics required for security authentication.

[0157] "Means for access control" refers to technology that manages access permissions to systems and services based on the results of voice authentication.

[0158] The present invention relates to a system for recording, analyzing, and synthesizing a user's voice and making the generated voice data available. A specific method for implementing this system will be described below.

[0159] 1. Recording and sending

[0160] The user records their own voice using a smartphone or computer. The user launches the application and taps the "Start Recording" button to begin recording. The user speaks a specified phrase and taps the "Stop Recording" button when recording is complete. The recorded voice data is temporarily stored in the device's local storage. The device then generates and sends an HTTP request to send the recorded data to the server.

[0161] 2. Audio analysis and profile generation

[0162] Once the server receives the voice data, it analyzes it using a speech recognition engine (for example, Google Cloud Speech-to-Text API or VOSK), which generates a voice profile from the voice data. This voice profile contains the individual characteristics of the voice data and is later used for transcription to text and voice authentication.

[0163] 3. Transcription and parameter setting

[0164] The server transcribes the text based on the voice profile, which converts the audio data into text format. Then, to set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile.

[0165] 4. Identification and synthetic speech generation

[0166] A unique identifier (e.g., hash value or NFT) is assigned to the voice data to prevent unauthorized use or forgery of the data. The server uses the configured parameters to launch a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech) to generate synthetic speech. For example, based on the text "Hello, my name is User," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[0167] 5. Download and Use

[0168] To use the generated synthesized speech, the user downloads it from the application. When the user taps the "Download generated speech" button, the device sends an HTTP request to the server, requesting specific speech data. The server receives the request and sends the generated synthesized speech data to the device. The device then saves the received speech data locally and makes it playable.

[0169] Specific examples

[0170] As a concrete example, consider voice authentication when a user logs in to a security system. The user launches an application on their smartphone and says, "My name is ____. Please allow me to log in." The recorded voice is sent to the server, which generates a voice profile. The generated voice profile is compared with pre-registered voice characteristics to perform authentication. If authentication is successful, access to the security system is granted.

[0171] Example prompt sentence:

[0172] Simply say the phrase "My name is ____. Please allow me to log in." Your recording will be run through a speech recognition engine and converted into text. A voice profile will then be generated that will be used for security authentication.

[0173] In this way, the present invention provides a highly accurate and safe voice authentication system that effectively utilizes the user's voice.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] A user starts an application on a smartphone or computer and taps the "Start Recording" button to start recording. The input is the user's voice, and the output is the recorded voice data. The recorded voice data is temporarily stored in the device's local storage.

[0177] Step 2:

[0178] When the user finishes recording, they tap the "Stop Recording" button. The input is the user's operation, and the output is the command to stop recording. The recorded audio data is saved in the device's local storage.

[0179] Step 3:

[0180] The device sends the recorded data to the server. The input is the recorded audio data, and the output is an HTTP request to the server. This request contains the audio data.

[0181] Step 4:

[0182] The server receives the voice data and analyzes it using a speech recognition engine. The input is the voice data sent to the server, and the output is the analyzed voice profile. The speech recognition engine uses Google Cloud Speech-to-Text API or VOSK.

[0183] Step 5:

[0184] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. This data is stored in the server's database.

[0185] Step 6:

[0186] To set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile. The input is the voice profile, and the output is the set parameters.

[0187] Step 7:

[0188] The server assigns a unique identifier to the audio data. The input is the audio data, and the output is the audio data with the assigned identifier. This identifier is composed of a hash value, NFT, etc.

[0189] Step 8:

[0190] The server uses the configured parameters to start the speech synthesis engine and generate synthetic speech. The input is the configured parameters and text data, and the output is synthetic speech data. The speech synthesis engine used is Amazon Polly or Google Text-to-Speech.

[0191] Step 9:

[0192] The generated synthetic speech is stored in a database on the server. The input is the synthetic speech data, and the output is the synthetic speech data stored in the database.

[0193] Step 10:

[0194] To use the generated synthesized speech, the user taps the "Download generated speech" button in the application. The input is the user's download request, and the output is an HTTP request to the server.

[0195] Step 11:

[0196] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the HTTP request for download, and the output is the synthesized voice data.

[0197] Step 12:

[0198] The device stores the received voice data locally and makes it playable. The input is the synthesized voice data from the server, and the output is the locally stored voice data.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. Below, we will explain the program processing of this system in natural language and provide a detailed description with specific examples of its use.

[0201] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. The recorded voice data is temporarily stored in the device's local storage.

[0202] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0203] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[0204] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[0205] With the introduction of the emotion engine, the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, speed, etc. of the voice and adds that information to the voice profile. The recognized emotion information is used in the process of generating synthetic voice.

[0206] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server sets the tone of the synthesized voice to be brighter and the speed to be faster.

[0207] The server assigns a unique identifier (e.g., NFT) to the audio data to prevent unauthorized use or forgery. The audio data with the assigned identifier is then stored in a database.

[0208] In generating synthetic speech, the server uses the set parameters to start the speech synthesis engine and generate synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[0209] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[0210] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and have other users purchase them, creating a new source of revenue.

[0211] In this way, the present invention provides a system that effectively utilizes the user's voice, solves various voice problems, enables the generation of natural voice that reflects the user's emotions, and further enables the monetization of voice data.

[0212] The processing flow will be explained below.

[0213] Step 1:

[0214] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[0215] Step 2:

[0216] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0217] Step 3:

[0218] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[0219] Step 4:

[0220] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[0221] Step 5:

[0222] The server recognizes emotions. The server uses an emotion engine to analyze the user's emotions from the voice data. The emotion engine analyzes characteristics of the voice, such as tone, rhythm, and speed, to identify the user's emotions and adds the results to the voice profile.

[0223] Step 6:

[0224] The server sets the parameters of the synthesized voice. The server adjusts the parameters of the synthesized voice (sound quality, tone, speed, etc.) based on the voice profile and the results of the emotion engine. For example, if the user expresses joy, the server will set the tone of the synthesized voice to be brighter and the speed to be faster.

[0225] Step 7:

[0226] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[0227] Step 8:

[0228] The server generates synthetic speech. The server activates a speech synthesis engine based on the set parameters to generate synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[0229] Step 9:

[0230] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[0231] Step 10:

[0232] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[0233] Example 2

[0234] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0235] Conventional speech synthesis systems have had difficulty generating natural-sounding speech that reflects the user's emotions. Furthermore, proper identifiers were not assigned to voice data to prevent unauthorized use or forgery, and the authenticity of the voice data could not be guaranteed. Furthermore, there was no way for users to easily download and use synthesized voices.

[0236] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0237] In this invention, the server includes means for recognizing and adding emotional information from voice data using an emotion engine, means for setting parameters for synthetic voice based on the emotional information, and means for assigning a unique identifier to the voice data. This makes it possible to generate natural synthetic voice that reflects the user's emotions and realize an environment in which users can easily use voice data while ensuring high authenticity of the voice data.

[0238] "User" refers to any individual or entity that uses the System.

[0239] "Audio Data" refers to the digital form of audio recorded by a user.

[0240] "Server" refers to the computing resources for receiving, analyzing, storing, and otherwise processing audio data.

[0241] "Audio profile" refers to information about audio characteristics obtained by analyzing audio data.

[0242] "Transcription" refers to the process of analyzing audio data and converting it into text form.

[0243] An "emotion engine" refers to a program that recognizes a user's emotions from voice data and obtains that information.

[0244] "Emotion information" refers to data relating to the user's emotions recognized from the voice data.

[0245] "Synthetic voice" refers to an artificial voice generated based on a voice profile and emotional information.

[0246] "Unique identifier" refers to a unique identification code assigned to voice data to prevent unauthorized use or forgery.

[0247] "Parameters" refer to the setting items used when generating synthetic speech.

[0248] "Database" refers to a system that stores voice data and related information in an organized manner.

[0249] This invention relates to a system that records, analyzes, and synthesizes a user's voice, and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. The following describes the specific names of each piece of hardware and software, as well as details of data processing and data calculation.

[0250] First, the user records their own voice using the device. The user launches an application on their smartphone or computer (e.g., a dedicated recording app) and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. At this stage, the recorded voice data is temporarily stored in the device's local storage.

[0251] Next, the device sends the recorded voice data to the server. The device generates an HTTP request and sends the recorded data to the server. The server receives this request and obtains the voice data. A voice recognition engine (for example, Google Speech-to-Text API or Azure (registered trademark) Cognitive Services) is used to analyze the voice data. This generates a voice profile from the voice data and converts it into text format. The generated text is stored in a database on the server.

[0252] Using an emotion engine (e.g., IBM Watson® Tone Analyzer), the server recognizes the user's emotion from the voice data. The emotion engine identifies the user's emotion from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. This emotion information is used in the synthetic voice generation process.

[0253] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[0254] The server then assigns a unique identifier (such as a unique identification code or an NFT using blockchain technology) to the audio data to prevent unauthorized use or counterfeiting. The audio data with the assigned identifier is then stored in a database.

[0255] To generate synthetic speech, the server uses the set parameters to launch a speech synthesis engine (for example, Google Text-to-Speech API or Amazon Polly) and generates synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is then saved back into the database.

[0256] Finally, the user can use the generated voice data. For example, when a user taps the "Download generated voice" button in an application, the device sends an HTTP request to the server to obtain specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data in local storage and makes it playable.

[0257] For example, if a user suddenly loses their voice during a presentation, they can deliver their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and earn new revenue by having other users purchase them.

[0258] Examples of prompts include, "If I lose my voice during a presentation, how can I communicate my message using pre-recorded or pre-generated synthesized speech?" and "How can I put my voice on the marketplace so other users can purchase it?"

[0259] This system effectively utilizes the user's voice, solves various voice problems, and enables natural voice generation that reflects the user's emotions. Furthermore, it also allows for monetization of voice data.

[0260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0261] Step 1:

[0262] The user launches the application on their device and taps the "Start Recording" button.

[0263] Input: User operation (tapping the start recording button)

[0264] Output: Start of the recording process

[0265] Specific operation: The user launches the app on their smartphone, goes to the recording screen, and presses the start recording button. The microphone is enabled and audio begins to be recorded.

[0266] Step 2:

[0267] The user speaks their message and taps the "Stop Recording" button.

[0268] Input: User's voice, tapping the stop recording button

[0269] Output: Recorded audio data

[0270] Specific operation: When the user finishes speaking, they press the stop recording button. The recording will stop and the audio data will be saved in the device's local storage.

[0271] Step 3:

[0272] The device generates an HTTP request to send the recorded audio data to the server.

[0273] Input: Recorded audio data

[0274] Output: HTTP request

[0275] Specific behavior: The app generates an HTTP request containing the recording data and metadata (recording date and time, user ID, etc.).

[0276] Step 4:

[0277] The device sends an HTTP request to the server.

[0278] Input: HTTP request

[0279] Output: Send data to the server

[0280] What happens: Your device sends audio data to a server using a Wi-Fi or mobile data connection.

[0281] Step 5:

[0282] The server receives the HTTP request and retrieves the audio data.

[0283] Input: HTTP request

[0284] Output: Save audio data

[0285] Specific operation: The server receives the request at the API endpoint and saves the audio data to storage.

[0286] Step 6:

[0287] The server uses a speech recognition engine to analyze the voice data and convert it into text format.

[0288] Input: Audio data

[0289] Output: Audio profile and text data

[0290] Specific operation: Calls a speech recognition engine (e.g., Google Speech-to-Text API), analyzes the voice data, converts it into text, and saves the generated text in a database.

[0291] Step 7:

[0292] The server uses an emotion engine to recognize the user's emotion from the voice data and adds the emotion information to the voice profile.

[0293] Input: Audio data, text data

[0294] Output: Voice profile with added emotional information

[0295] What it does: An emotion engine (e.g., IBM Watson Tone Analyzer) analyzes tone, rhythm, and speed to identify the user's emotion and add it to the voice profile.

[0296] Step 8:

[0297] The server sets the parameters of the synthesized voice based on the emotion information.

[0298] Input: A voice profile with added emotional information

[0299] Output: Synthesized voice parameters

[0300] Specific operation: If the emotion is "joy," the parameters of the synthesized voice are set, such as making the tone brighter and the speed faster.

[0301] Step 9:

[0302] The server assigns a unique identifier to the audio data.

[0303] Input: Audio data

[0304] Output: Audio data with identifiers

[0305] Specific operation: Generate a unique identification code (e.g., NFT) and embed it in the audio data. Store the audio data with the identifier in a database.

[0306] Step 10:

[0307] The server starts the speech synthesis engine and generates synthetic speech using the configured parameters.

[0308] Input: Synthesized voice parameters, text data

[0309] Output: Synthesized voice data

[0310] Specific operation: Calls a speech synthesis engine (e.g., Google Text-to-Speech API) and generates synthetic speech using the text and voice profile as input. The generated speech data is saved in a database.

[0311] Step 11:

[0312] The user downloads the generated audio data.

[0313] Input: User download request

[0314] Output: Audio data sent to the device

[0315] Specific operation: When a user taps the "Download generated audio" button in the application, the device sends an HTTP request to the server, requesting specific audio data. The server receives the request and sends the generated synthesized audio data to the device. The device then saves the received audio data in local storage and makes it playable.

[0316] In this way, each processing step involves a specific operation, and the system functions with the roles of the user, terminal, and server allotted according to their roles.

[0317] (Application example 2)

[0318] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0319] When a user uses a food delivery service, it is difficult for customer support to quickly and accurately determine the user's emotions and respond appropriately to those emotions. In particular, when a customer is dissatisfied or encounters a problem, it is important to properly understand the customer's emotions and respond accordingly to improve service satisfaction. This invention aims to realize a natural voice response that reflects the user's emotions and improves the user experience.

[0320] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0321] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data in the server and generating a voice profile, means for recognizing the user's emotion from the voice data using an emotion recognition engine, means for setting parameters for synthetic voice based on the recognized emotion information, means for assigning a unique identifier to the voice data, means for generating synthetic voice, and means for the user to download the generated voice data. This enables a response using synthetic voice that reflects the user's emotion, thereby improving satisfaction with the food delivery service.

[0322] "Means for recording the user's voice" refers to equipment or software that records the user's voice as digital data.

[0323] "Means for transmitting recorded audio to a server" refers to a function that transfers recorded audio data to a remote server using the Internet or other communication methods.

[0324] "Means for analyzing voice data on the server and generating a voice profile" refers to a function for analyzing voice data sent to the server and generating a voice profile that indicates the individual characteristics of the data.

[0325] "Means for transcribing based on an audio profile" refers to a function for converting audio data into text data based on an audio profile.

[0326] "Means for recognizing a user's emotions from voice data using an emotion recognition engine" refers to a function that analyzes elements such as tone and rhythm from voice data and identifies the user's emotions.

[0327] "Means for setting parameters of synthesized voice based on recognized emotional information" refers to a function for adjusting settings such as tone and speed of synthesized voice based on recognized emotional information.

[0328] "Means for assigning a unique identifier to audio data" refers to a function that assigns a unique identifier to audio data to prevent unauthorized use or forgery of the data.

[0329] The "means for generating synthetic speech" refers to a function that activates a speech synthesis engine using set parameters to generate natural-sounding speech.

[0330] "Means for users to download generated voice data" refers to a function that transmits the generated synthesized voice data from the server to the user's terminal and makes it available for download.

[0331] The present invention provides a system for providing utterances that reflect the user's emotions in customer support for food delivery services. The system can be implemented using a variety of hardware and software.

[0332] First, the user records audio using a smartphone. Launch the application on the smartphone, tap the "Start Recording" button to begin recording, and speak the message. When recording is finished, tap the "Stop Recording" button. The recorded audio data is temporarily stored in the smartphone's local storage.

[0333] Next, the smartphone sends the recorded data to the server. The smartphone generates an HTTP request and sends it to the server. The server receives the request and retrieves the audio data.

[0334] Once the server receives the audio data, it analyzes it using a speech recognition engine (e.g., Google Speech-to-Text API), which generates a voice profile from the audio data. This voice profile is a dataset of the audio data's individual characteristics, which is used to transcribe it into text.

[0335] The server then transcribes the text based on the voice profile, converting the voice data into text format. The server stores the generated text in a database. Using an emotion recognition engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. The recognized emotion information is used in the synthetic voice generation process.

[0336] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as tone, speed, etc.) based on the emotion recognized by the emotion recognition engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[0337] The server assigns a unique identifier to the voice data. The voice data with the assigned identifier is stored in a database. To generate synthetic voice, the server uses the configured parameters to launch a voice synthesis engine (e.g., Amazon Polly) and generates synthetic voice. The generated voice data is stored in a database.

[0338] To use the generated voice data, the user downloads the generated voice from the smartphone application. When the user taps the "Download generated voice" button, the smartphone sends an HTTP request to the server, requesting the acquisition of specific voice data. The server receives the request and sends the generated synthesized voice data to the smartphone. The smartphone then stores the received voice data locally and makes it playable.

[0339] As a concrete example, consider when a user reports a problem with a food delivery service to telephone support. At this time, the user can pre-record utterances that reflect their emotions and convey their feelings appropriately. Furthermore, if the customer support understands the user's emotions and responds appropriately to the situation, customer satisfaction with food delivery services can be significantly improved.

[0340] Example prompts to input to a generative AI model:

[0341] Analyze the user's voice recording, recognize emotions based on the tone, rhythm, and rate of the voice, and adjust the tone and rate of the synthesized voice accordingly, such as making the tone brighter and faster if the user is expressing joy.

[0342] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0343] Step 1:

[0344] The user starts the smartphone app and taps the "Start Recording" button to begin recording their voice. When the user has finished speaking, they tap the "Stop Recording" button to stop recording. This operation saves the user's speech as digital data in local storage. The input is the user's voice, and the output is voice data.

[0345] Step 2:

[0346] The device acquires the recorded voice data, generates an HTTP request, and sends it to the server. The input is voice data, and the output is an HTTP request. Specifically, the device converts the voice data into binary format and sends it to the server.

[0347] Step 3:

[0348] The server receives the HTTP request and extracts the audio data. It then analyzes the audio data using a speech recognition engine (e.g., Google Speech-to-Text API) and generates a voice profile. The input is the audio data and the output is the voice profile. The server converts the audio file to text and stores the text in a database.

[0349] Step 4:

[0350] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. Specifically, the voice recognition engine analyzes the features obtained from the voice data and converts it into text data.

[0351] Step 5:

[0352] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions from text data. The input is text data, and the output is emotional information. Specifically, the server analyzes the tone, rhythm, and speed of the text and assigns an emotional label (e.g., joy, sadness, anger).

[0353] Step 6:

[0354] The server sets the parameters of the synthesized speech (sound quality, tone, speed, etc.) based on the emotional information. The input is the emotional information, and the output is the parameters of the synthesized speech. Specifically, the characteristics of the synthesized speech are dynamically adjusted according to the emotional information.

[0355] Step 7:

[0356] The server assigns a unique identifier to the audio data. The input is audio data, and the output is audio data with an identifier. Specifically, a unique identifier is generated using an identifier generation algorithm and added to the audio data.

[0357] Step 8:

[0358] The server uses a speech synthesis engine (e.g., Amazon Polly) to generate synthetic speech based on the set parameters. The input is the set parameters, and the output is the synthesized speech data. Specifically, the speech synthesis engine takes text and parameters as input and generates natural-sounding speech.

[0359] Step 9:

[0360] When a user taps the "Download generated voice" button on the smartphone app, the device sends an HTTP request to the server to request specific synthesized voice data. The input is the user request, and the output is an HTTP request.

[0361] Step 10:

[0362] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the user request and the output is the synthesized voice data. Specifically, the server searches for the voice data stored and sends the corresponding voice data to the terminal.

[0363] Step 11:

[0364] The device stores the received synthesized speech data locally and makes it playable. The input is synthesized speech data, and the output is playable speech data. Specifically, the device stores the data in an appropriate format and plays it back on an audio player or similar device so that the user can listen to it.

[0365] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0366] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0367] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0371] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0372] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0373] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0374] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0375] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0376] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0377] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0378] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0379] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0380] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0381] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. The program processing of this system is explained in natural language below, and is described in detail with specific examples.

[0382] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and starts recording by tapping the "Start Recording" button. The user speaks their message and taps the "Stop Recording" button when recording is finished. The recorded voice data is temporarily stored in the device's local storage.

[0383] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request. The server receives this request and obtains the audio data.

[0384] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[0385] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[0386] To set the parameters of the synthesized voice, the server sets the necessary parameters based on the voice profile, including sound quality, tone, speed, etc. The server then assigns a unique identifier (e.g., NFT) to the voice data to prevent unauthorized use or forgery of the data.

[0387] In generating synthetic speech, the server uses the set parameters to activate the speech synthesis engine and generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The server stores the generated synthetic speech in a database.

[0388] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[0389] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[0390] In this way, the present invention provides a system that can effectively utilize user voices, solve various voice problems, and monetize voice data.

[0391] The processing flow will be explained below.

[0392] Step 1:

[0393] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[0394] Step 2:

[0395] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0396] Step 3:

[0397] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[0398] Step 4:

[0399] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[0400] Step 5:

[0401] The server sets the parameters of the synthesized voice. The server uses a voice profile to set the parameters required for the synthesized voice (quality, tone, speed, etc.). This setting allows the synthesized voice to be created to reflect the user's individual voice characteristics.

[0402] Step 6:

[0403] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[0404] Step 7:

[0405] The server generates synthetic speech. The server starts a speech synthesis engine based on the set parameters and generates synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics is generated. The generated speech data is stored in a database.

[0406] Step 8:

[0407] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[0408] Step 9:

[0409] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[0410] Example 1

[0411] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0412] Conventional systems that use voice data require a great deal of manual work and specialized knowledge for users to effectively utilize voice data. For example, each step, such as recording, analyzing, transcribing, and generating synthesized voice data, must be performed manually, which poses a significant time and labor cost. Furthermore, there are insufficient measures in place to prevent unauthorized use or forgery of voice data, which poses security challenges. Therefore, there is a need for a system that allows users to easily collect, analyze, synthesize, and utilize voice data.

[0413] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0414] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to an information processing device, means for analyzing the voice data in the information processing device and generating a voice profile, means for transcribing based on the voice profile, means for setting parameters for the synthetic voice, means for assigning an identification code to the voice data, means for generating the synthetic voice, and means for the user to download the generated voice data. This allows users to easily record, analyze, and transcribe their own voice data and use it as synthetic voice, while also ensuring security to prevent fraudulent use and counterfeiting.

[0415] "User" refers to an individual who uses this system.

[0416] "Voice" refers to human speech and other sounds that travel as vibrations through the air (sound waves).

[0417] "Recording" refers to the act of recording sound as digital data using an electronic device.

[0418] "Information processing device" is a general term for electronic devices that perform processes such as saving, analyzing, and transmitting data, and here it mainly refers to servers.

[0419] "Audio data" refers to data that represents recorded audio in digital form.

[0420] An "audio profile" refers to a data set that extracts individual features and characteristics of audio data.

[0421] "Transcription" refers to the conversion of audio data into text format.

[0422] "Synthetic speech" refers to speech that is artificially generated based on text data.

[0423] "Parameters" refer to variables that set audio characteristics, such as quality, tone, and speed.

[0424] The "identification code" refers to a code that is assigned to audio data as unique identification information.

[0425] A "voice recognition engine" refers to software or hardware that analyzes voice data and converts it into text data.

[0426] "Identification code generation algorithm" refers to a calculation method for generating a unique identification code for audio data.

[0427] "Downloading" refers to transferring and storing audio data from an information processing device to another electronic device.

[0428] The present invention provides a system that efficiently acquires, analyzes, and generates voice data, allowing users to effectively utilize the data. A specific method for implementing this system will be described below.

[0429] The user uses the device to record their own voice. The user launches an application on their smartphone or computer (e.g., the "Voice Recorder" app) and taps the "Start Recording" button to begin recording. When recording is finished, the user taps the "Stop Recording" button. The recorded voice data is temporarily stored in the device's local storage.

[0430] To send the recorded audio data to the server, the device generates an HTTP request that includes the audio file. The server receives this request and uses the HTTP protocol to retrieve the audio data. Specifically, the device includes all audio data meta information in the request header and attaches the audio file to the request body.

[0431] When the server receives the voice data, it calls a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the voice data. The engine identifies the phonemes and words in the voice data and converts it into text format. It then generates a voice profile, which contains the individual characteristics of the voice data.

[0432] Based on the voice profile, the server converts the voice data into text format, and the generated text is stored in the server's database. It also sets voice synthesis parameters (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data to prevent fraud and counterfeiting.

[0433] Next, the server invokes a speech synthesis engine (e.g., "Amazon Polly") using the set parameters to generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[0434] To use the generated voice data, the user must tap the "Download generated voice" button in the application. The device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then stores the received voice data locally and makes it playable.

[0435] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[0436] An example of a prompt sentence to input to the generative AI model is as follows:

[0437] "Hello, my name is Taro Tanaka. I'd like to move on to the next slide in my presentation."

[0438] In this way, the present invention provides a system that makes effective use of the user's voice, solves various voice problems, and further monetizes voice data.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] The user launches an application on the device and taps the "Start Recording" button, which activates the device's built-in microphone and captures the user's voice as digital data.

[0442] Input: User utterance

[0443] Output: Digital audio data (e.g., "Data being recorded")

[0444] Step 2:

[0445] When the user has finished recording, they tap the "Stop Recording" button, which saves the digital audio data to the device's local storage.

[0446] Input: Data being recorded

[0447] Output: Audio file saved in the device's local storage (e.g., WAV format file)

[0448] Step 3:

[0449] The device generates an HTTP request to send the recorded audio data to the server, attaches the audio file to this request, and sends it to the server.

[0450] Input: Audio files stored in the device's local storage

[0451] Output: HTTP request sent to the server

[0452] Step 4:

[0453] The server receives the HTTP request, retrieves the provided voice data, and passes the received voice data to the speech recognition engine.

[0454] Input: HTTP request sent from the terminal

[0455] Output: Speech data fed to the speech recognition engine

[0456] Step 5:

[0457] The server uses a speech recognition engine (e.g., a "Speech Recognition Service API") to analyze the audio data and generate a speech profile, which is a data set containing the features of the audio data.

[0458] Input: Audio data

[0459] Output: Audio Profile

[0460] Step 6:

[0461] The server converts the voice data into text based on the voice profile, and the generated text data is stored in the server's database.

[0462] Input: Audio Profile

[0463] Output: Text data stored in the database

[0464] Step 7:

[0465] The server sets the parameters of the synthesized voice (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data. The identification code is important to prevent fraud and forgery.

[0466] Input: Audio Profile

[0467] Output: Audio data with identification code

[0468] Step 8:

[0469] The server invokes a speech synthesis engine (e.g., a "speech synthesis service") using the configured parameters to generate a synthesized voice. This voice file is stored in the server's database.

[0470] Input: Audio data with parameters set

[0471] Output: Synthesized speech files stored in a database

[0472] Step 9:

[0473] The user taps the "Download generated audio" button, which causes the device to send an HTTP request to the server to retrieve the specific audio data.

[0474] Input: User download request

[0475] Output: HTTP request to the server

[0476] Step 10:

[0477] The server receives the HTTP request and returns the generated synthesized voice data to the terminal.

[0478] Input: HTTP request

[0479] Output: Synthesized voice data sent to the device

[0480] Step 11:

[0481] The device saves the received synthesized voice data locally and prepares it for playback. Specifically, it saves the voice file and prepares it for playback on a media player.

[0482] Input: Synthesized voice data sent from the server

[0483] Output: Locally saved playable audio file

[0484] Through the above steps, the present invention enables efficient recording, analysis, transcription, and reuse of user speech as synthesized speech.

[0485] (Application example 1)

[0486] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0487] Conventional voice authentication systems have issues with voice recognition accuracy and security, and are not sufficiently effective in fields requiring advanced authentication. Furthermore, there is a lack of means to prevent unauthorized use of voices that exploit the voice characteristics of individual users. The object of this invention is to provide a highly accurate and secure authentication system that utilizes user voice data, thereby improving the reliability and convenience of voice authentication.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0489] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data and generating a voice profile in the server, means for transcribing based on the voice profile, means for setting parameters for synthetic voice, means for assigning a unique identifier to the voice data, means for generating synthetic voice, means for the user to download the generated voice data, means for performing security authentication using the user's voice, means for storing a voice profile of phrases required for security authentication, and means for performing access control based on the results of voice authentication, thereby enabling highly accurate and secure voice authentication.

[0490] "User voice" refers to the user's vocalizations that are recorded and analyzed by the system.

[0491] A "means for recording" is a function that uses a device such as a microphone to capture the user's voice in digital form.

[0492] "Means of transmission" refers to the technology for transferring recorded data to a server via the Internet or a network.

[0493] The "means for analyzing and generating a voice profile" is a function that uses a voice recognition engine to analyze voice data and compile voice characteristics into a data set.

[0494] "Transcribing means" refers to a technology that converts audio data into text format based on an audio profile.

[0495] The "means for setting parameters of synthesized speech" is a function for setting speech characteristics such as sound quality, tone, and speed.

[0496] "Means for assigning an identifier" refers to a technology for assigning a unique identification code to audio data.

[0497] "Means for generating synthetic voice" refers to technology that generates artificial voice that reproduces the user's voice characteristics based on set parameters.

[0498] The "means for downloading" is a function for importing the generated audio data into the user's device.

[0499] "Means for performing security authentication" refers to technology that uses the user's voice to perform identity authentication.

[0500] A "means for storing voice profiles" is a technology that stores a data set of voice characteristics required for security authentication.

[0501] "Means for access control" refers to technology that manages access permissions to systems and services based on the results of voice authentication.

[0502] The present invention relates to a system for recording, analyzing, and synthesizing a user's voice and making the generated voice data available. A specific method for implementing this system will be described below.

[0503] 1. Recording and sending

[0504] The user records their own voice using a smartphone or computer. The user launches the application and taps the "Start Recording" button to begin recording. The user speaks a specified phrase and taps the "Stop Recording" button when recording is complete. The recorded voice data is temporarily stored in the device's local storage. The device then generates and sends an HTTP request to send the recorded data to the server.

[0505] 2. Audio analysis and profile generation

[0506] Once the server receives the voice data, it analyzes it using a speech recognition engine (for example, Google Cloud Speech-to-Text API or VOSK), which generates a voice profile from the voice data. This voice profile contains the individual characteristics of the voice data and is later used for transcription to text and voice authentication.

[0507] 3. Transcription and parameter setting

[0508] The server transcribes the text based on the voice profile, which converts the audio data into text format. Then, to set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile.

[0509] 4. Identification and synthetic speech generation

[0510] A unique identifier (e.g., hash value or NFT) is assigned to the voice data to prevent unauthorized use or forgery of the data. The server uses the configured parameters to launch a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech) to generate synthetic speech. For example, based on the text "Hello, my name is User," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[0511] 5. Download and Use

[0512] To use the generated synthesized speech, the user downloads it from the application. When the user taps the "Download generated speech" button, the device sends an HTTP request to the server, requesting specific speech data. The server receives the request and sends the generated synthesized speech data to the device. The device then saves the received speech data locally and makes it playable.

[0513] Specific examples

[0514] As a concrete example, consider voice authentication when a user logs in to a security system. The user launches an application on their smartphone and says, "My name is ____. Please allow me to log in." The recorded voice is sent to the server, which generates a voice profile. The generated voice profile is compared with pre-registered voice characteristics to perform authentication. If authentication is successful, access to the security system is granted.

[0515] Example prompt sentence:

[0516] Simply say the phrase "My name is ____. Please allow me to log in." Your recording will be run through a speech recognition engine and converted into text. A voice profile will then be generated that will be used for security authentication.

[0517] In this way, the present invention provides a highly accurate and safe voice authentication system that effectively utilizes the user's voice.

[0518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0519] Step 1:

[0520] A user starts an application on a smartphone or computer and taps the "Start Recording" button to start recording. The input is the user's voice, and the output is the recorded voice data. The recorded voice data is temporarily stored in the device's local storage.

[0521] Step 2:

[0522] When the user finishes recording, they tap the "Stop Recording" button. The input is the user's operation, and the output is the command to stop recording. The recorded audio data is saved in the device's local storage.

[0523] Step 3:

[0524] The device sends the recorded data to the server. The input is the recorded audio data, and the output is an HTTP request to the server. This request contains the audio data.

[0525] Step 4:

[0526] The server receives the voice data and analyzes it using a speech recognition engine. The input is the voice data sent to the server, and the output is the analyzed voice profile. The speech recognition engine uses Google Cloud Speech-to-Text API or VOSK.

[0527] Step 5:

[0528] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. This data is stored in the server's database.

[0529] Step 6:

[0530] To set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile. The input is the voice profile, and the output is the set parameters.

[0531] Step 7:

[0532] The server assigns a unique identifier to the audio data. The input is the audio data, and the output is the audio data with the assigned identifier. This identifier is composed of a hash value, NFT, etc.

[0533] Step 8:

[0534] The server uses the configured parameters to start the speech synthesis engine and generate synthetic speech. The input is the configured parameters and text data, and the output is synthetic speech data. The speech synthesis engine used is Amazon Polly or Google Text-to-Speech.

[0535] Step 9:

[0536] The generated synthetic speech is stored in a database on the server. The input is the synthetic speech data, and the output is the synthetic speech data stored in the database.

[0537] Step 10:

[0538] To use the generated synthesized speech, the user taps the "Download generated speech" button in the application. The input is the user's download request, and the output is an HTTP request to the server.

[0539] Step 11:

[0540] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the HTTP request for download, and the output is the synthesized voice data.

[0541] Step 12:

[0542] The device stores the received voice data locally and makes it playable. The input is the synthesized voice data from the server, and the output is the locally stored voice data.

[0543] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0544] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. Below, we will explain the program processing of this system in natural language and provide a detailed description with specific examples of its use.

[0545] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. The recorded voice data is temporarily stored in the device's local storage.

[0546] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0547] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[0548] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[0549] With the introduction of the emotion engine, the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, speed, etc. of the voice and adds that information to the voice profile. The recognized emotion information is used in the process of generating synthetic voice.

[0550] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server sets the tone of the synthesized voice to be brighter and the speed to be faster.

[0551] The server assigns a unique identifier (e.g., NFT) to the audio data to prevent unauthorized use or forgery. The audio data with the assigned identifier is then stored in a database.

[0552] In generating synthetic speech, the server uses the set parameters to start the speech synthesis engine and generate synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[0553] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[0554] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and have other users purchase them, creating a new source of revenue.

[0555] In this way, the present invention provides a system that effectively utilizes the user's voice, solves various voice problems, enables the generation of natural voice that reflects the user's emotions, and further enables the monetization of voice data.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[0559] Step 2:

[0560] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0561] Step 3:

[0562] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[0563] Step 4:

[0564] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[0565] Step 5:

[0566] The server recognizes emotions. The server uses an emotion engine to analyze the user's emotions from the voice data. The emotion engine analyzes characteristics of the voice, such as tone, rhythm, and speed, to identify the user's emotions and adds the results to the voice profile.

[0567] Step 6:

[0568] The server sets the parameters of the synthesized voice. The server adjusts the parameters of the synthesized voice (sound quality, tone, speed, etc.) based on the voice profile and the results of the emotion engine. For example, if the user expresses joy, the server will set the tone of the synthesized voice to be brighter and the speed to be faster.

[0569] Step 7:

[0570] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[0571] Step 8:

[0572] The server generates synthetic speech. The server activates a speech synthesis engine based on the set parameters to generate synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[0573] Step 9:

[0574] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[0575] Step 10:

[0576] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[0577] Example 2

[0578] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] Conventional speech synthesis systems have had difficulty generating natural-sounding speech that reflects the user's emotions. Furthermore, proper identifiers were not assigned to voice data to prevent unauthorized use or forgery, and the authenticity of the voice data could not be guaranteed. Furthermore, there was no way for users to easily download and use synthesized voices.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0581] In this invention, the server includes means for recognizing and adding emotional information from voice data using an emotion engine, means for setting parameters for synthetic voice based on the emotional information, and means for assigning a unique identifier to the voice data. This makes it possible to generate natural synthetic voice that reflects the user's emotions and realize an environment in which users can easily use voice data while ensuring high authenticity of the voice data.

[0582] "User" refers to any individual or entity that uses the System.

[0583] "Audio Data" refers to the digital form of audio recorded by a user.

[0584] "Server" refers to the computing resources for receiving, analyzing, storing, and otherwise processing audio data.

[0585] "Audio profile" refers to information about audio characteristics obtained by analyzing audio data.

[0586] "Transcription" refers to the process of analyzing audio data and converting it into text form.

[0587] An "emotion engine" refers to a program that recognizes a user's emotions from voice data and obtains that information.

[0588] "Emotion information" refers to data relating to the user's emotions recognized from the voice data.

[0589] "Synthetic voice" refers to an artificial voice generated based on a voice profile and emotional information.

[0590] "Unique identifier" refers to a unique identification code assigned to voice data to prevent unauthorized use or forgery.

[0591] "Parameters" refer to the setting items used when generating synthetic speech.

[0592] "Database" refers to a system that stores voice data and related information in an organized manner.

[0593] This invention relates to a system that records, analyzes, and synthesizes a user's voice, and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. The following describes the specific names of each piece of hardware and software, as well as details of data processing and data calculation.

[0594] First, the user records their own voice using the device. The user launches an application on their smartphone or computer (e.g., a dedicated recording app) and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. At this stage, the recorded voice data is temporarily stored in the device's local storage.

[0595] Next, the device sends the recorded voice data to the server. The device generates an HTTP request and sends the recorded data to the server. The server receives this request and retrieves the voice data. A voice recognition engine (for example, Google Speech-to-Text API or Azure Cognitive Services) is used to analyze the voice data. This generates a voice profile from the voice data and converts it into text format. The generated text is stored in a database on the server.

[0596] Using an emotion engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotion from the voice data. The emotion engine identifies the user's emotion from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. This emotion information is used in the synthetic voice generation process.

[0597] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[0598] The server then assigns a unique identifier (such as a unique identification code or an NFT using blockchain technology) to the audio data to prevent unauthorized use or counterfeiting. The audio data with the assigned identifier is then stored in a database.

[0599] To generate synthetic speech, the server uses the set parameters to launch a speech synthesis engine (for example, Google Text-to-Speech API or Amazon Polly) and generates synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is then saved back into the database.

[0600] Finally, the user can use the generated voice data. For example, when a user taps the "Download generated voice" button in an application, the device sends an HTTP request to the server to obtain specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data in local storage and makes it playable.

[0601] For example, if a user suddenly loses their voice during a presentation, they can deliver their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and earn new revenue by having other users purchase them.

[0602] Examples of prompts include, "If I lose my voice during a presentation, how can I communicate my message using pre-recorded or pre-generated synthesized speech?" and "How can I put my voice on the marketplace so other users can purchase it?"

[0603] This system effectively utilizes the user's voice, solves various voice problems, and enables natural voice generation that reflects the user's emotions. Furthermore, it also allows for monetization of voice data.

[0604] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0605] Step 1:

[0606] The user launches the application on their device and taps the "Start Recording" button.

[0607] Input: User operation (tapping the start recording button)

[0608] Output: Start of the recording process

[0609] Specific operation: The user launches the app on their smartphone, goes to the recording screen, and presses the start recording button. The microphone is enabled and audio begins to be recorded.

[0610] Step 2:

[0611] The user speaks their message and taps the "Stop Recording" button.

[0612] Input: User's voice, tapping the stop recording button

[0613] Output: Recorded audio data

[0614] Specific operation: When the user finishes speaking, they press the stop recording button. The recording will stop and the audio data will be saved in the device's local storage.

[0615] Step 3:

[0616] The device generates an HTTP request to send the recorded audio data to the server.

[0617] Input: Recorded audio data

[0618] Output: HTTP request

[0619] Specific behavior: The app generates an HTTP request containing the recording data and metadata (recording date and time, user ID, etc.).

[0620] Step 4:

[0621] The device sends an HTTP request to the server.

[0622] Input: HTTP request

[0623] Output: Send data to the server

[0624] What happens: Your device sends audio data to a server using a Wi-Fi or mobile data connection.

[0625] Step 5:

[0626] The server receives the HTTP request and retrieves the audio data.

[0627] Input: HTTP request

[0628] Output: Save audio data

[0629] Specific operation: The server receives the request at the API endpoint and saves the audio data to storage.

[0630] Step 6:

[0631] The server uses a speech recognition engine to analyze the voice data and convert it into text format.

[0632] Input: Audio data

[0633] Output: Audio profile and text data

[0634] Specific operation: Calls a speech recognition engine (e.g., Google Speech-to-Text API), analyzes the voice data, converts it into text, and saves the generated text in a database.

[0635] Step 7:

[0636] The server uses an emotion engine to recognize the user's emotion from the voice data and adds the emotion information to the voice profile.

[0637] Input: Audio data, text data

[0638] Output: Voice profile with added emotional information

[0639] What it does: An emotion engine (e.g., IBM Watson Tone Analyzer) analyzes tone, rhythm, and speed to identify the user's emotion and add it to the voice profile.

[0640] Step 8:

[0641] The server sets the parameters of the synthesized voice based on the emotion information.

[0642] Input: A voice profile with added emotional information

[0643] Output: Synthesized voice parameters

[0644] Specific operation: If the emotion is "joy," the parameters of the synthesized voice are set, such as making the tone brighter and the speed faster.

[0645] Step 9:

[0646] The server assigns a unique identifier to the audio data.

[0647] Input: Audio data

[0648] Output: Audio data with identifiers

[0649] Specific operation: Generate a unique identification code (e.g., NFT) and embed it in the audio data. Store the audio data with the identifier in a database.

[0650] Step 10:

[0651] The server starts the speech synthesis engine and generates synthetic speech using the configured parameters.

[0652] Input: Synthesized voice parameters, text data

[0653] Output: Synthesized voice data

[0654] Specific operation: Calls a speech synthesis engine (e.g., Google Text-to-Speech API) and generates synthetic speech using the text and voice profile as input. The generated speech data is saved in a database.

[0655] Step 11:

[0656] The user downloads the generated audio data.

[0657] Input: User download request

[0658] Output: Audio data sent to the device

[0659] Specific operation: When a user taps the "Download generated audio" button in the application, the device sends an HTTP request to the server, requesting specific audio data. The server receives the request and sends the generated synthesized audio data to the device. The device then saves the received audio data in local storage and makes it playable.

[0660] In this way, each processing step involves a specific operation, and the system functions with the roles of the user, terminal, and server allotted according to their roles.

[0661] (Application example 2)

[0662] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] When a user uses a food delivery service, it is difficult for customer support to quickly and accurately determine the user's emotions and respond appropriately to those emotions. In particular, when a customer is dissatisfied or encounters a problem, it is important to properly understand the customer's emotions and respond accordingly to improve service satisfaction. This invention aims to realize a natural voice response that reflects the user's emotions and improves the user experience.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0665] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data in the server and generating a voice profile, means for recognizing the user's emotion from the voice data using an emotion recognition engine, means for setting parameters for synthetic voice based on the recognized emotion information, means for assigning a unique identifier to the voice data, means for generating synthetic voice, and means for the user to download the generated voice data. This enables a response using synthetic voice that reflects the user's emotion, thereby improving satisfaction with the food delivery service.

[0666] "Means for recording the user's voice" refers to equipment or software that records the user's voice as digital data.

[0667] "Means for transmitting recorded audio to a server" refers to a function that transfers recorded audio data to a remote server using the Internet or other communication methods.

[0668] "Means for analyzing voice data on the server and generating a voice profile" refers to a function for analyzing voice data sent to the server and generating a voice profile that indicates the individual characteristics of the data.

[0669] "Means for transcribing based on an audio profile" refers to a function for converting audio data into text data based on an audio profile.

[0670] "Means for recognizing a user's emotions from voice data using an emotion recognition engine" refers to a function that analyzes elements such as tone and rhythm from voice data and identifies the user's emotions.

[0671] "Means for setting parameters of synthesized voice based on recognized emotional information" refers to a function for adjusting settings such as tone and speed of synthesized voice based on recognized emotional information.

[0672] "Means for assigning a unique identifier to audio data" refers to a function that assigns a unique identifier to audio data to prevent unauthorized use or forgery of the data.

[0673] The "means for generating synthetic speech" refers to a function that activates a speech synthesis engine using set parameters to generate natural-sounding speech.

[0674] "Means for users to download generated voice data" refers to a function that transmits the generated synthesized voice data from the server to the user's terminal and makes it available for download.

[0675] The present invention provides a system for providing utterances that reflect the user's emotions in customer support for food delivery services. The system can be implemented using a variety of hardware and software.

[0676] First, the user records audio using a smartphone. Launch the application on the smartphone, tap the "Start Recording" button to begin recording, and speak the message. When recording is finished, tap the "Stop Recording" button. The recorded audio data is temporarily stored in the smartphone's local storage.

[0677] Next, the smartphone sends the recorded data to the server. The smartphone generates an HTTP request and sends it to the server. The server receives the request and retrieves the audio data.

[0678] Once the server receives the audio data, it analyzes it using a speech recognition engine (e.g., Google Speech-to-Text API), which generates a voice profile from the audio data. This voice profile is a dataset of the audio data's individual characteristics, which is used to transcribe it into text.

[0679] The server then transcribes the text based on the voice profile, converting the voice data into text format. The server stores the generated text in a database. Using an emotion recognition engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. The recognized emotion information is used in the synthetic voice generation process.

[0680] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as tone, speed, etc.) based on the emotion recognized by the emotion recognition engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[0681] The server assigns a unique identifier to the voice data. The voice data with the assigned identifier is stored in a database. To generate synthetic voice, the server uses the configured parameters to launch a voice synthesis engine (e.g., Amazon Polly) and generates synthetic voice. The generated voice data is stored in a database.

[0682] To use the generated voice data, the user downloads the generated voice from the smartphone application. When the user taps the "Download generated voice" button, the smartphone sends an HTTP request to the server, requesting the acquisition of specific voice data. The server receives the request and sends the generated synthesized voice data to the smartphone. The smartphone then stores the received voice data locally and makes it playable.

[0683] As a concrete example, consider when a user reports a problem with a food delivery service to telephone support. At this time, the user can pre-record utterances that reflect their emotions and convey their feelings appropriately. Furthermore, if the customer support understands the user's emotions and responds appropriately to the situation, customer satisfaction with food delivery services can be significantly improved.

[0684] Example prompts to input to a generative AI model:

[0685] Analyze the user's voice recording, recognize emotions based on the tone, rhythm, and rate of the voice, and adjust the tone and rate of the synthesized voice accordingly, such as making the tone brighter and faster if the user is expressing joy.

[0686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0687] Step 1:

[0688] The user starts the smartphone app and taps the "Start Recording" button to begin recording their voice. When the user has finished speaking, they tap the "Stop Recording" button to stop recording. This operation saves the user's speech as digital data in local storage. The input is the user's voice, and the output is voice data.

[0689] Step 2:

[0690] The device acquires the recorded voice data, generates an HTTP request, and sends it to the server. The input is voice data, and the output is an HTTP request. Specifically, the device converts the voice data into binary format and sends it to the server.

[0691] Step 3:

[0692] The server receives the HTTP request and extracts the audio data. It then analyzes the audio data using a speech recognition engine (e.g., Google Speech-to-Text API) and generates a voice profile. The input is the audio data and the output is the voice profile. The server converts the audio file to text and stores the text in a database.

[0693] Step 4:

[0694] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. Specifically, the voice recognition engine analyzes the features obtained from the voice data and converts it into text data.

[0695] Step 5:

[0696] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions from text data. The input is text data, and the output is emotional information. Specifically, the server analyzes the tone, rhythm, and speed of the text and assigns an emotional label (e.g., joy, sadness, anger).

[0697] Step 6:

[0698] The server sets the parameters of the synthesized speech (sound quality, tone, speed, etc.) based on the emotional information. The input is the emotional information, and the output is the parameters of the synthesized speech. Specifically, the characteristics of the synthesized speech are dynamically adjusted according to the emotional information.

[0699] Step 7:

[0700] The server assigns a unique identifier to the audio data. The input is audio data, and the output is audio data with an identifier. Specifically, a unique identifier is generated using an identifier generation algorithm and added to the audio data.

[0701] Step 8:

[0702] The server uses a speech synthesis engine (e.g., Amazon Polly) to generate synthetic speech based on the set parameters. The input is the set parameters, and the output is the synthesized speech data. Specifically, the speech synthesis engine takes text and parameters as input and generates natural-sounding speech.

[0703] Step 9:

[0704] When a user taps the "Download generated voice" button on the smartphone app, the device sends an HTTP request to the server to request specific synthesized voice data. The input is the user request, and the output is an HTTP request.

[0705] Step 10:

[0706] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the user request and the output is the synthesized voice data. Specifically, the server searches for the voice data stored and sends the corresponding voice data to the terminal.

[0707] Step 11:

[0708] The device stores the received synthesized speech data locally and makes it playable. The input is synthesized speech data, and the output is playable speech data. Specifically, the device stores the data in an appropriate format and plays it back on an audio player or similar device so that the user can listen to it.

[0709] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0710] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0711] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0712] [Third embodiment]

[0713] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0714] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0715] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0716] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0717] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0718] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0719] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0720] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0721] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0722] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0723] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0724] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0725] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. The program processing of this system is explained in natural language below, and is described in detail with specific examples.

[0726] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and starts recording by tapping the "Start Recording" button. The user speaks their message and taps the "Stop Recording" button when recording is finished. The recorded voice data is temporarily stored in the device's local storage.

[0727] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request. The server receives this request and obtains the audio data.

[0728] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[0729] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[0730] To set the parameters of the synthesized voice, the server sets the necessary parameters based on the voice profile, including sound quality, tone, speed, etc. The server then assigns a unique identifier (e.g., NFT) to the voice data to prevent unauthorized use or forgery of the data.

[0731] In generating synthetic speech, the server uses the set parameters to activate the speech synthesis engine and generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The server stores the generated synthetic speech in a database.

[0732] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[0733] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[0734] In this way, the present invention provides a system that can effectively utilize user voices, solve various voice problems, and monetize voice data.

[0735] The processing flow will be explained below.

[0736] Step 1:

[0737] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[0738] Step 2:

[0739] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0740] Step 3:

[0741] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[0742] Step 4:

[0743] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[0744] Step 5:

[0745] The server sets the parameters of the synthesized voice. The server uses a voice profile to set the parameters required for the synthesized voice (quality, tone, speed, etc.). This setting allows the synthesized voice to be created to reflect the user's individual voice characteristics.

[0746] Step 6:

[0747] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[0748] Step 7:

[0749] The server generates synthetic speech. The server starts a speech synthesis engine based on the set parameters and generates synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics is generated. The generated speech data is stored in a database.

[0750] Step 8:

[0751] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[0752] Step 9:

[0753] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[0754] Example 1

[0755] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0756] Conventional systems that use voice data require a great deal of manual work and specialized knowledge for users to effectively utilize voice data. For example, each step, such as recording, analyzing, transcribing, and generating synthesized voice data, must be performed manually, which poses a significant time and labor cost. Furthermore, there are insufficient measures in place to prevent unauthorized use or forgery of voice data, which poses security challenges. Therefore, there is a need for a system that allows users to easily collect, analyze, synthesize, and utilize voice data.

[0757] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0758] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to an information processing device, means for analyzing the voice data in the information processing device and generating a voice profile, means for transcribing based on the voice profile, means for setting parameters for the synthetic voice, means for assigning an identification code to the voice data, means for generating the synthetic voice, and means for the user to download the generated voice data. This allows users to easily record, analyze, and transcribe their own voice data and use it as synthetic voice, while also ensuring security to prevent fraudulent use and counterfeiting.

[0759] "User" refers to an individual who uses this system.

[0760] "Voice" refers to human speech and other sounds that travel as vibrations through the air (sound waves).

[0761] "Recording" refers to the act of recording sound as digital data using an electronic device.

[0762] "Information processing device" is a general term for electronic devices that perform processes such as saving, analyzing, and transmitting data, and here it mainly refers to servers.

[0763] "Audio data" refers to data that represents recorded audio in digital form.

[0764] An "audio profile" refers to a data set that extracts individual features and characteristics of audio data.

[0765] "Transcription" refers to the conversion of audio data into text format.

[0766] "Synthetic speech" refers to speech that is artificially generated based on text data.

[0767] "Parameters" refer to variables that set audio characteristics, such as quality, tone, and speed.

[0768] The "identification code" refers to a code that is assigned to audio data as unique identification information.

[0769] A "voice recognition engine" refers to software or hardware that analyzes voice data and converts it into text data.

[0770] "Identification code generation algorithm" refers to a calculation method for generating a unique identification code for audio data.

[0771] "Downloading" refers to transferring and storing audio data from an information processing device to another electronic device.

[0772] The present invention provides a system that efficiently acquires, analyzes, and generates voice data, allowing users to effectively utilize the data. A specific method for implementing this system will be described below.

[0773] The user uses the device to record their own voice. The user launches an application on their smartphone or computer (e.g., the "Voice Recorder" app) and taps the "Start Recording" button to begin recording. When recording is finished, the user taps the "Stop Recording" button. The recorded voice data is temporarily stored in the device's local storage.

[0774] To send the recorded audio data to the server, the device generates an HTTP request that includes the audio file. The server receives this request and uses the HTTP protocol to retrieve the audio data. Specifically, the device includes all audio data meta information in the request header and attaches the audio file to the request body.

[0775] When the server receives the voice data, it calls a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the voice data. The engine identifies the phonemes and words in the voice data and converts it into text format. It then generates a voice profile, which contains the individual characteristics of the voice data.

[0776] Based on the voice profile, the server converts the voice data into text format, and the generated text is stored in the server's database. It also sets voice synthesis parameters (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data to prevent fraud and counterfeiting.

[0777] Next, the server invokes a speech synthesis engine (e.g., "Amazon Polly") using the set parameters to generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[0778] To use the generated voice data, the user must tap the "Download generated voice" button in the application. The device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then stores the received voice data locally and makes it playable.

[0779] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[0780] An example of a prompt sentence to input to the generative AI model is as follows:

[0781] "Hello, my name is Taro Tanaka. I'd like to move on to the next slide in my presentation."

[0782] In this way, the present invention provides a system that makes effective use of the user's voice, solves various voice problems, and further monetizes voice data.

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] Step 1:

[0785] The user launches an application on the device and taps the "Start Recording" button, which activates the device's built-in microphone and captures the user's voice as digital data.

[0786] Input: User utterance

[0787] Output: Digital audio data (e.g., "Data being recorded")

[0788] Step 2:

[0789] When the user has finished recording, they tap the "Stop Recording" button, which saves the digital audio data to the device's local storage.

[0790] Input: Data being recorded

[0791] Output: Audio file saved in the device's local storage (e.g., WAV format file)

[0792] Step 3:

[0793] The device generates an HTTP request to send the recorded audio data to the server, attaches the audio file to this request, and sends it to the server.

[0794] Input: Audio files stored in the device's local storage

[0795] Output: HTTP request sent to the server

[0796] Step 4:

[0797] The server receives the HTTP request, retrieves the provided voice data, and passes the received voice data to the speech recognition engine.

[0798] Input: HTTP request sent from the terminal

[0799] Output: Speech data fed to the speech recognition engine

[0800] Step 5:

[0801] The server uses a speech recognition engine (e.g., a "Speech Recognition Service API") to analyze the audio data and generate a speech profile, which is a data set containing the features of the audio data.

[0802] Input: Audio data

[0803] Output: Audio Profile

[0804] Step 6:

[0805] The server converts the voice data into text based on the voice profile, and the generated text data is stored in the server's database.

[0806] Input: Audio Profile

[0807] Output: Text data stored in the database

[0808] Step 7:

[0809] The server sets the parameters of the synthesized voice (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data. The identification code is important to prevent fraud and forgery.

[0810] Input: Audio Profile

[0811] Output: Audio data with identification code

[0812] Step 8:

[0813] The server invokes a speech synthesis engine (e.g., a "speech synthesis service") using the configured parameters to generate a synthesized voice. This voice file is stored in the server's database.

[0814] Input: Audio data with parameters set

[0815] Output: Synthesized speech files stored in a database

[0816] Step 9:

[0817] The user taps the "Download generated audio" button, which causes the device to send an HTTP request to the server to retrieve the specific audio data.

[0818] Input: User download request

[0819] Output: HTTP request to the server

[0820] Step 10:

[0821] The server receives the HTTP request and returns the generated synthesized voice data to the terminal.

[0822] Input: HTTP request

[0823] Output: Synthesized voice data sent to the device

[0824] Step 11:

[0825] The device saves the received synthesized voice data locally and prepares it for playback. Specifically, it saves the voice file and prepares it for playback on a media player.

[0826] Input: Synthesized voice data sent from the server

[0827] Output: Locally saved playable audio file

[0828] Through the above steps, the present invention enables efficient recording, analysis, transcription, and reuse of user speech as synthesized speech.

[0829] (Application example 1)

[0830] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0831] Conventional voice authentication systems have issues with voice recognition accuracy and security, and are not sufficiently effective in fields requiring advanced authentication. Furthermore, there is a lack of means to prevent unauthorized use of voices that exploit the voice characteristics of individual users. The object of this invention is to provide a highly accurate and secure authentication system that utilizes user voice data, thereby improving the reliability and convenience of voice authentication.

[0832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0833] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data and generating a voice profile in the server, means for transcribing based on the voice profile, means for setting parameters for synthetic voice, means for assigning a unique identifier to the voice data, means for generating synthetic voice, means for the user to download the generated voice data, means for performing security authentication using the user's voice, means for storing a voice profile of phrases required for security authentication, and means for performing access control based on the results of voice authentication, thereby enabling highly accurate and secure voice authentication.

[0834] "User voice" refers to the user's vocalizations that are recorded and analyzed by the system.

[0835] A "means for recording" is a function that uses a device such as a microphone to capture the user's voice in digital form.

[0836] "Means of transmission" refers to the technology for transferring recorded data to a server via the Internet or a network.

[0837] The "means for analyzing and generating a voice profile" is a function that uses a voice recognition engine to analyze voice data and compile voice characteristics into a data set.

[0838] "Transcribing means" refers to a technology that converts audio data into text format based on an audio profile.

[0839] The "means for setting parameters of synthesized speech" is a function for setting speech characteristics such as sound quality, tone, and speed.

[0840] "Means for assigning an identifier" refers to a technology for assigning a unique identification code to audio data.

[0841] "Means for generating synthetic voice" refers to technology that generates artificial voice that reproduces the user's voice characteristics based on set parameters.

[0842] The "means for downloading" is a function for importing the generated audio data into the user's device.

[0843] "Means for performing security authentication" refers to technology that uses the user's voice to perform identity authentication.

[0844] A "means for storing voice profiles" is a technology that stores a data set of voice characteristics required for security authentication.

[0845] "Means for access control" refers to technology that manages access permissions to systems and services based on the results of voice authentication.

[0846] The present invention relates to a system for recording, analyzing, and synthesizing a user's voice and making the generated voice data available. A specific method for implementing this system will be described below.

[0847] 1. Recording and sending

[0848] The user records their own voice using a smartphone or computer. The user launches the application and taps the "Start Recording" button to begin recording. The user speaks a specified phrase and taps the "Stop Recording" button when recording is complete. The recorded voice data is temporarily stored in the device's local storage. The device then generates and sends an HTTP request to send the recorded data to the server.

[0849] 2. Audio analysis and profile generation

[0850] Once the server receives the voice data, it analyzes it using a speech recognition engine (for example, Google Cloud Speech-to-Text API or VOSK), which generates a voice profile from the voice data. This voice profile contains the individual characteristics of the voice data and is later used for transcription to text and voice authentication.

[0851] 3. Transcription and parameter setting

[0852] The server transcribes the text based on the voice profile, which converts the audio data into text format. Then, to set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile.

[0853] 4. Identification and synthetic speech generation

[0854] A unique identifier (e.g., hash value or NFT) is assigned to the voice data to prevent unauthorized use or forgery of the data. The server uses the configured parameters to launch a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech) to generate synthetic speech. For example, based on the text "Hello, my name is User," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[0855] 5. Download and Use

[0856] To use the generated synthesized speech, the user downloads it from the application. When the user taps the "Download generated speech" button, the device sends an HTTP request to the server, requesting specific speech data. The server receives the request and sends the generated synthesized speech data to the device. The device then saves the received speech data locally and makes it playable.

[0857] Specific examples

[0858] As a concrete example, consider voice authentication when a user logs in to a security system. The user launches an application on their smartphone and says, "My name is ____. Please allow me to log in." The recorded voice is sent to the server, which generates a voice profile. The generated voice profile is compared with pre-registered voice characteristics to perform authentication. If authentication is successful, access to the security system is granted.

[0859] Example prompt sentence:

[0860] Simply say the phrase "My name is ____. Please allow me to log in." Your recording will be run through a speech recognition engine and converted into text. A voice profile will then be generated that will be used for security authentication.

[0861] In this way, the present invention provides a highly accurate and safe voice authentication system that effectively utilizes the user's voice.

[0862] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0863] Step 1:

[0864] A user starts an application on a smartphone or computer and taps the "Start Recording" button to start recording. The input is the user's voice, and the output is the recorded voice data. The recorded voice data is temporarily stored in the device's local storage.

[0865] Step 2:

[0866] When the user finishes recording, they tap the "Stop Recording" button. The input is the user's operation, and the output is the command to stop recording. The recorded audio data is saved in the device's local storage.

[0867] Step 3:

[0868] The device sends the recorded data to the server. The input is the recorded audio data, and the output is an HTTP request to the server. This request contains the audio data.

[0869] Step 4:

[0870] The server receives the voice data and analyzes it using a speech recognition engine. The input is the voice data sent to the server, and the output is the analyzed voice profile. The speech recognition engine uses Google Cloud Speech-to-Text API or VOSK.

[0871] Step 5:

[0872] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. This data is stored in the server's database.

[0873] Step 6:

[0874] To set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile. The input is the voice profile, and the output is the set parameters.

[0875] Step 7:

[0876] The server assigns a unique identifier to the audio data. The input is the audio data, and the output is the audio data with the assigned identifier. This identifier is composed of a hash value, NFT, etc.

[0877] Step 8:

[0878] The server uses the configured parameters to start the speech synthesis engine and generate synthetic speech. The input is the configured parameters and text data, and the output is synthetic speech data. The speech synthesis engine used is Amazon Polly or Google Text-to-Speech.

[0879] Step 9:

[0880] The generated synthetic speech is stored in a database on the server. The input is the synthetic speech data, and the output is the synthetic speech data stored in the database.

[0881] Step 10:

[0882] To use the generated synthesized speech, the user taps the "Download generated speech" button in the application. The input is the user's download request, and the output is an HTTP request to the server.

[0883] Step 11:

[0884] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the HTTP request for download, and the output is the synthesized voice data.

[0885] Step 12:

[0886] The device stores the received voice data locally and makes it playable. The input is the synthesized voice data from the server, and the output is the locally stored voice data.

[0887] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0888] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. Below, we will explain the program processing of this system in natural language and provide a detailed description with specific examples of its use.

[0889] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. The recorded voice data is temporarily stored in the device's local storage.

[0890] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0891] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[0892] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[0893] With the introduction of the emotion engine, the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, speed, etc. of the voice and adds that information to the voice profile. The recognized emotion information is used in the process of generating synthetic voice.

[0894] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server sets the tone of the synthesized voice to be brighter and the speed to be faster.

[0895] The server assigns a unique identifier (e.g., NFT) to the audio data to prevent unauthorized use or forgery. The audio data with the assigned identifier is then stored in a database.

[0896] In generating synthetic speech, the server uses the set parameters to start the speech synthesis engine and generate synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[0897] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[0898] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and have other users purchase them, creating a new source of revenue.

[0899] In this way, the present invention provides a system that effectively utilizes the user's voice, solves various voice problems, enables the generation of natural voice that reflects the user's emotions, and further enables the monetization of voice data.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[0903] Step 2:

[0904] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[0905] Step 3:

[0906] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[0907] Step 4:

[0908] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[0909] Step 5:

[0910] The server recognizes emotions. The server uses an emotion engine to analyze the user's emotions from the voice data. The emotion engine analyzes characteristics of the voice, such as tone, rhythm, and speed, to identify the user's emotions and adds the results to the voice profile.

[0911] Step 6:

[0912] The server sets the parameters of the synthesized voice. The server adjusts the parameters of the synthesized voice (sound quality, tone, speed, etc.) based on the voice profile and the results of the emotion engine. For example, if the user expresses joy, the server will set the tone of the synthesized voice to be brighter and the speed to be faster.

[0913] Step 7:

[0914] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[0915] Step 8:

[0916] The server generates synthetic speech. The server activates a speech synthesis engine based on the set parameters to generate synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[0917] Step 9:

[0918] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[0919] Step 10:

[0920] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[0921] Example 2

[0922] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0923] Conventional speech synthesis systems have had difficulty generating natural-sounding speech that reflects the user's emotions. Furthermore, proper identifiers were not assigned to voice data to prevent unauthorized use or forgery, and the authenticity of the voice data could not be guaranteed. Furthermore, there was no way for users to easily download and use synthesized voices.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0925] In this invention, the server includes means for recognizing and adding emotional information from voice data using an emotion engine, means for setting parameters for synthetic voice based on the emotional information, and means for assigning a unique identifier to the voice data. This makes it possible to generate natural synthetic voice that reflects the user's emotions and realize an environment in which users can easily use voice data while ensuring high authenticity of the voice data.

[0926] "User" refers to any individual or entity that uses the System.

[0927] "Audio Data" refers to the digital form of audio recorded by a user.

[0928] "Server" refers to the computing resources for receiving, analyzing, storing, and otherwise processing audio data.

[0929] "Audio profile" refers to information about audio characteristics obtained by analyzing audio data.

[0930] "Transcription" refers to the process of analyzing audio data and converting it into text form.

[0931] An "emotion engine" refers to a program that recognizes a user's emotions from voice data and obtains that information.

[0932] "Emotion information" refers to data relating to the user's emotions recognized from the voice data.

[0933] "Synthetic voice" refers to an artificial voice generated based on a voice profile and emotional information.

[0934] "Unique identifier" refers to a unique identification code assigned to voice data to prevent unauthorized use or forgery.

[0935] "Parameters" refer to the setting items used when generating synthetic speech.

[0936] "Database" refers to a system that stores voice data and related information in an organized manner.

[0937] This invention relates to a system that records, analyzes, and synthesizes a user's voice, and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. The following describes the specific names of each piece of hardware and software, as well as details of data processing and data calculation.

[0938] First, the user records their own voice using the device. The user launches an application on their smartphone or computer (e.g., a dedicated recording app) and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. At this stage, the recorded voice data is temporarily stored in the device's local storage.

[0939] Next, the device sends the recorded voice data to the server. The device generates an HTTP request and sends the recorded data to the server. The server receives this request and retrieves the voice data. A voice recognition engine (for example, Google Speech-to-Text API or Azure Cognitive Services) is used to analyze the voice data. This generates a voice profile from the voice data and converts it into text format. The generated text is stored in a database on the server.

[0940] Using an emotion engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotion from the voice data. The emotion engine identifies the user's emotion from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. This emotion information is used in the synthetic voice generation process.

[0941] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[0942] The server then assigns a unique identifier (such as a unique identification code or an NFT using blockchain technology) to the audio data to prevent unauthorized use or counterfeiting. The audio data with the assigned identifier is then stored in a database.

[0943] To generate synthetic speech, the server uses the set parameters to launch a speech synthesis engine (for example, Google Text-to-Speech API or Amazon Polly) and generates synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is then saved back into the database.

[0944] Finally, the user can use the generated voice data. For example, when a user taps the "Download generated voice" button in an application, the device sends an HTTP request to the server to obtain specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data in local storage and makes it playable.

[0945] For example, if a user suddenly loses their voice during a presentation, they can deliver their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and earn new revenue by having other users purchase them.

[0946] Examples of prompts include, "If I lose my voice during a presentation, how can I communicate my message using pre-recorded or pre-generated synthesized speech?" and "How can I put my voice on the marketplace so other users can purchase it?"

[0947] This system effectively utilizes the user's voice, solves various voice problems, and enables natural voice generation that reflects the user's emotions. Furthermore, it also allows for monetization of voice data.

[0948] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] The user launches the application on their device and taps the "Start Recording" button.

[0951] Input: User operation (tapping the start recording button)

[0952] Output: Start of the recording process

[0953] Specific operation: The user launches the app on their smartphone, goes to the recording screen, and presses the start recording button. The microphone is enabled and audio begins to be recorded.

[0954] Step 2:

[0955] The user speaks their message and taps the "Stop Recording" button.

[0956] Input: User's voice, tapping the stop recording button

[0957] Output: Recorded audio data

[0958] Specific operation: When the user finishes speaking, they press the stop recording button. The recording will stop and the audio data will be saved in the device's local storage.

[0959] Step 3:

[0960] The device generates an HTTP request to send the recorded audio data to the server.

[0961] Input: Recorded audio data

[0962] Output: HTTP request

[0963] Specific behavior: The app generates an HTTP request containing the recording data and metadata (recording date and time, user ID, etc.).

[0964] Step 4:

[0965] The device sends an HTTP request to the server.

[0966] Input: HTTP request

[0967] Output: Send data to the server

[0968] What happens: Your device sends audio data to a server using a Wi-Fi or mobile data connection.

[0969] Step 5:

[0970] The server receives the HTTP request and retrieves the audio data.

[0971] Input: HTTP request

[0972] Output: Save audio data

[0973] Specific operation: The server receives the request at the API endpoint and saves the audio data to storage.

[0974] Step 6:

[0975] The server uses a speech recognition engine to analyze the voice data and convert it into text format.

[0976] Input: Audio data

[0977] Output: Audio profile and text data

[0978] Specific operation: Calls a speech recognition engine (e.g., Google Speech-to-Text API), analyzes the voice data, converts it into text, and saves the generated text in a database.

[0979] Step 7:

[0980] The server uses an emotion engine to recognize the user's emotion from the voice data and adds the emotion information to the voice profile.

[0981] Input: Audio data, text data

[0982] Output: Voice profile with added emotional information

[0983] What it does: An emotion engine (e.g., IBM Watson Tone Analyzer) analyzes tone, rhythm, and speed to identify the user's emotion and add it to the voice profile.

[0984] Step 8:

[0985] The server sets the parameters of the synthesized voice based on the emotion information.

[0986] Input: A voice profile with added emotional information

[0987] Output: Synthesized voice parameters

[0988] Specific operation: If the emotion is "joy," the parameters of the synthesized voice are set, such as making the tone brighter and the speed faster.

[0989] Step 9:

[0990] The server assigns a unique identifier to the audio data.

[0991] Input: Audio data

[0992] Output: Audio data with identifiers

[0993] Specific operation: Generate a unique identification code (e.g., NFT) and embed it in the audio data. Store the audio data with the identifier in a database.

[0994] Step 10:

[0995] The server starts the speech synthesis engine and generates synthetic speech using the configured parameters.

[0996] Input: Synthesized voice parameters, text data

[0997] Output: Synthesized voice data

[0998] Specific operation: Calls a speech synthesis engine (e.g., Google Text-to-Speech API) and generates synthetic speech using the text and voice profile as input. The generated speech data is saved in a database.

[0999] Step 11:

[1000] The user downloads the generated audio data.

[1001] Input: User download request

[1002] Output: Audio data sent to the device

[1003] Specific operation: When a user taps the "Download generated audio" button in the application, the device sends an HTTP request to the server, requesting specific audio data. The server receives the request and sends the generated synthesized audio data to the device. The device then saves the received audio data in local storage and makes it playable.

[1004] In this way, each processing step involves a specific operation, and the system functions with the roles of the user, terminal, and server allotted according to their roles.

[1005] (Application example 2)

[1006] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1007] When a user uses a food delivery service, it is difficult for customer support to quickly and accurately determine the user's emotions and respond appropriately to those emotions. In particular, when a customer is dissatisfied or encounters a problem, it is important to properly understand the customer's emotions and respond accordingly to improve service satisfaction. This invention aims to realize a natural voice response that reflects the user's emotions and improves the user experience.

[1008] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1009] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data in the server and generating a voice profile, means for recognizing the user's emotion from the voice data using an emotion recognition engine, means for setting parameters for synthetic voice based on the recognized emotion information, means for assigning a unique identifier to the voice data, means for generating synthetic voice, and means for the user to download the generated voice data. This enables a response using synthetic voice that reflects the user's emotion, thereby improving satisfaction with the food delivery service.

[1010] "Means for recording the user's voice" refers to equipment or software that records the user's voice as digital data.

[1011] "Means for transmitting recorded audio to a server" refers to a function that transfers recorded audio data to a remote server using the Internet or other communication methods.

[1012] "Means for analyzing voice data on the server and generating a voice profile" refers to a function for analyzing voice data sent to the server and generating a voice profile that indicates the individual characteristics of the data.

[1013] "Means for transcribing based on an audio profile" refers to a function for converting audio data into text data based on an audio profile.

[1014] "Means for recognizing a user's emotions from voice data using an emotion recognition engine" refers to a function that analyzes elements such as tone and rhythm from voice data and identifies the user's emotions.

[1015] "Means for setting parameters of synthesized voice based on recognized emotional information" refers to a function for adjusting settings such as tone and speed of synthesized voice based on recognized emotional information.

[1016] "Means for assigning a unique identifier to audio data" refers to a function that assigns a unique identifier to audio data to prevent unauthorized use or forgery of the data.

[1017] The "means for generating synthetic speech" refers to a function that activates a speech synthesis engine using set parameters to generate natural-sounding speech.

[1018] "Means for users to download generated voice data" refers to a function that transmits the generated synthesized voice data from the server to the user's terminal and makes it available for download.

[1019] The present invention provides a system for providing utterances that reflect the user's emotions in customer support for food delivery services. The system can be implemented using a variety of hardware and software.

[1020] First, the user records audio using a smartphone. Launch the application on the smartphone, tap the "Start Recording" button to begin recording, and speak the message. When recording is finished, tap the "Stop Recording" button. The recorded audio data is temporarily stored in the smartphone's local storage.

[1021] Next, the smartphone sends the recorded data to the server. The smartphone generates an HTTP request and sends it to the server. The server receives the request and retrieves the audio data.

[1022] Once the server receives the audio data, it analyzes it using a speech recognition engine (e.g., Google Speech-to-Text API), which generates a voice profile from the audio data. This voice profile is a dataset of the audio data's individual characteristics, which is used to transcribe it into text.

[1023] The server then transcribes the text based on the voice profile, converting the voice data into text format. The server stores the generated text in a database. Using an emotion recognition engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. The recognized emotion information is used in the synthetic voice generation process.

[1024] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as tone, speed, etc.) based on the emotion recognized by the emotion recognition engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[1025] The server assigns a unique identifier to the voice data. The voice data with the assigned identifier is stored in a database. To generate synthetic voice, the server uses the configured parameters to launch a voice synthesis engine (e.g., Amazon Polly) and generates synthetic voice. The generated voice data is stored in a database.

[1026] To use the generated voice data, the user downloads the generated voice from the smartphone application. When the user taps the "Download generated voice" button, the smartphone sends an HTTP request to the server, requesting the acquisition of specific voice data. The server receives the request and sends the generated synthesized voice data to the smartphone. The smartphone then stores the received voice data locally and makes it playable.

[1027] As a concrete example, consider when a user reports a problem with a food delivery service to telephone support. At this time, the user can pre-record utterances that reflect their emotions and convey their feelings appropriately. Furthermore, if the customer support understands the user's emotions and responds appropriately to the situation, customer satisfaction with food delivery services can be significantly improved.

[1028] Example prompts to input to a generative AI model:

[1029] Analyze the user's voice recording, recognize emotions based on the tone, rhythm, and rate of the voice, and adjust the tone and rate of the synthesized voice accordingly, such as making the tone brighter and faster if the user is expressing joy.

[1030] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1031] Step 1:

[1032] The user starts the smartphone app and taps the "Start Recording" button to begin recording their voice. When the user has finished speaking, they tap the "Stop Recording" button to stop recording. This operation saves the user's speech as digital data in local storage. The input is the user's voice, and the output is voice data.

[1033] Step 2:

[1034] The device acquires the recorded voice data, generates an HTTP request, and sends it to the server. The input is voice data, and the output is an HTTP request. Specifically, the device converts the voice data into binary format and sends it to the server.

[1035] Step 3:

[1036] The server receives the HTTP request and extracts the audio data. It then analyzes the audio data using a speech recognition engine (e.g., Google Speech-to-Text API) and generates a voice profile. The input is the audio data and the output is the voice profile. The server converts the audio file to text and stores the text in a database.

[1037] Step 4:

[1038] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. Specifically, the voice recognition engine analyzes the features obtained from the voice data and converts it into text data.

[1039] Step 5:

[1040] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions from text data. The input is text data, and the output is emotional information. Specifically, the server analyzes the tone, rhythm, and speed of the text and assigns an emotional label (e.g., joy, sadness, anger).

[1041] Step 6:

[1042] The server sets the parameters of the synthesized speech (sound quality, tone, speed, etc.) based on the emotional information. The input is the emotional information, and the output is the parameters of the synthesized speech. Specifically, the characteristics of the synthesized speech are dynamically adjusted according to the emotional information.

[1043] Step 7:

[1044] The server assigns a unique identifier to the audio data. The input is audio data, and the output is audio data with an identifier. Specifically, a unique identifier is generated using an identifier generation algorithm and added to the audio data.

[1045] Step 8:

[1046] The server uses a speech synthesis engine (e.g., Amazon Polly) to generate synthetic speech based on the set parameters. The input is the set parameters, and the output is the synthesized speech data. Specifically, the speech synthesis engine takes text and parameters as input and generates natural-sounding speech.

[1047] Step 9:

[1048] When a user taps the "Download generated voice" button on the smartphone app, the device sends an HTTP request to the server to request specific synthesized voice data. The input is the user request, and the output is an HTTP request.

[1049] Step 10:

[1050] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the user request and the output is the synthesized voice data. Specifically, the server searches for the voice data stored and sends the corresponding voice data to the terminal.

[1051] Step 11:

[1052] The device stores the received synthesized speech data locally and makes it playable. The input is synthesized speech data, and the output is playable speech data. Specifically, the device stores the data in an appropriate format and plays it back on an audio player or similar device so that the user can listen to it.

[1053] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1054] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1055] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1056] [Fourth embodiment]

[1057] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1058] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1059] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1060] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1061] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1062] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1063] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1064] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1065] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1066] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1067] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1068] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1069] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1070] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. The program processing of this system is explained in natural language below, and is described in detail with specific examples.

[1071] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and starts recording by tapping the "Start Recording" button. The user speaks their message and taps the "Stop Recording" button when recording is finished. The recorded voice data is temporarily stored in the device's local storage.

[1072] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request. The server receives this request and obtains the audio data.

[1073] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[1074] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[1075] To set the parameters of the synthesized voice, the server sets the necessary parameters based on the voice profile, including sound quality, tone, speed, etc. The server then assigns a unique identifier (e.g., NFT) to the voice data to prevent unauthorized use or forgery of the data.

[1076] In generating synthetic speech, the server uses the set parameters to activate the speech synthesis engine and generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The server stores the generated synthetic speech in a database.

[1077] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[1078] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[1079] In this way, the present invention provides a system that can effectively utilize user voices, solve various voice problems, and monetize voice data.

[1080] The processing flow will be explained below.

[1081] Step 1:

[1082] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[1083] Step 2:

[1084] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[1085] Step 3:

[1086] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[1087] Step 4:

[1088] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[1089] Step 5:

[1090] The server sets the parameters of the synthesized voice. The server uses a voice profile to set the parameters required for the synthesized voice (quality, tone, speed, etc.). This setting allows the synthesized voice to be created to reflect the user's individual voice characteristics.

[1091] Step 6:

[1092] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[1093] Step 7:

[1094] The server generates synthetic speech. The server starts a speech synthesis engine based on the set parameters and generates synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics is generated. The generated speech data is stored in a database.

[1095] Step 8:

[1096] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[1097] Step 9:

[1098] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[1099] Example 1

[1100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1101] Conventional systems that use voice data require a great deal of manual work and specialized knowledge for users to effectively utilize voice data. For example, each step, such as recording, analyzing, transcribing, and generating synthesized voice data, must be performed manually, which poses a significant time and labor cost. Furthermore, there are insufficient measures in place to prevent unauthorized use or forgery of voice data, which poses security challenges. Therefore, there is a need for a system that allows users to easily collect, analyze, synthesize, and utilize voice data.

[1102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1103] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to an information processing device, means for analyzing the voice data in the information processing device and generating a voice profile, means for transcribing based on the voice profile, means for setting parameters for the synthetic voice, means for assigning an identification code to the voice data, means for generating the synthetic voice, and means for the user to download the generated voice data. This allows users to easily record, analyze, and transcribe their own voice data and use it as synthetic voice, while also ensuring security to prevent fraudulent use and counterfeiting.

[1104] "User" refers to an individual who uses this system.

[1105] "Voice" refers to human speech and other sounds that travel as vibrations through the air (sound waves).

[1106] "Recording" refers to the act of recording sound as digital data using an electronic device.

[1107] "Information processing device" is a general term for electronic devices that perform processes such as saving, analyzing, and transmitting data, and here it mainly refers to servers.

[1108] "Audio data" refers to data that represents recorded audio in digital form.

[1109] An "audio profile" refers to a data set that extracts individual features and characteristics of audio data.

[1110] "Transcription" refers to the conversion of audio data into text format.

[1111] "Synthetic speech" refers to speech that is artificially generated based on text data.

[1112] "Parameters" refer to variables that set audio characteristics, such as quality, tone, and speed.

[1113] The "identification code" refers to a code that is assigned to audio data as unique identification information.

[1114] A "voice recognition engine" refers to software or hardware that analyzes voice data and converts it into text data.

[1115] "Identification code generation algorithm" refers to a calculation method for generating a unique identification code for audio data.

[1116] "Downloading" refers to transferring and storing audio data from an information processing device to another electronic device.

[1117] The present invention provides a system that efficiently acquires, analyzes, and generates voice data, allowing users to effectively utilize the data. A specific method for implementing this system will be described below.

[1118] The user uses the device to record their own voice. The user launches an application on their smartphone or computer (e.g., the "Voice Recorder" app) and taps the "Start Recording" button to begin recording. When recording is finished, the user taps the "Stop Recording" button. The recorded voice data is temporarily stored in the device's local storage.

[1119] To send the recorded audio data to the server, the device generates an HTTP request that includes the audio file. The server receives this request and uses the HTTP protocol to retrieve the audio data. Specifically, the device includes all audio data meta information in the request header and attaches the audio file to the request body.

[1120] When the server receives the voice data, it calls a speech recognition engine (e.g., Google Cloud Speech-to-Text API) to analyze the voice data. The engine identifies the phonemes and words in the voice data and converts it into text format. It then generates a voice profile, which contains the individual characteristics of the voice data.

[1121] Based on the voice profile, the server converts the voice data into text format, and the generated text is stored in the server's database. It also sets voice synthesis parameters (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data to prevent fraud and counterfeiting.

[1122] Next, the server invokes a speech synthesis engine (e.g., "Amazon Polly") using the set parameters to generate synthetic speech. For example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[1123] To use the generated voice data, the user must tap the "Download generated voice" button in the application. The device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then stores the received voice data locally and makes it playable.

[1124] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded or generated synthesized voice. Users can also offer their voice on a marketplace and other users can purchase it, creating a new source of revenue.

[1125] An example of a prompt sentence to input to the generative AI model is as follows:

[1126] "Hello, my name is Taro Tanaka. I'd like to move on to the next slide in my presentation."

[1127] In this way, the present invention provides a system that makes effective use of the user's voice, solves various voice problems, and further monetizes voice data.

[1128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1129] Step 1:

[1130] The user launches an application on the device and taps the "Start Recording" button, which activates the device's built-in microphone and captures the user's voice as digital data.

[1131] Input: User utterance

[1132] Output: Digital audio data (e.g., "Data being recorded")

[1133] Step 2:

[1134] When the user has finished recording, they tap the "Stop Recording" button, which saves the digital audio data to the device's local storage.

[1135] Input: Data being recorded

[1136] Output: Audio file saved in the device's local storage (e.g., WAV format file)

[1137] Step 3:

[1138] The device generates an HTTP request to send the recorded audio data to the server, attaches the audio file to this request, and sends it to the server.

[1139] Input: Audio files stored in the device's local storage

[1140] Output: HTTP request sent to the server

[1141] Step 4:

[1142] The server receives the HTTP request, retrieves the provided voice data, and passes the received voice data to the speech recognition engine.

[1143] Input: HTTP request sent from the terminal

[1144] Output: Speech data fed to the speech recognition engine

[1145] Step 5:

[1146] The server uses a speech recognition engine (e.g., a "Speech Recognition Service API") to analyze the audio data and generate a speech profile, which is a data set containing the features of the audio data.

[1147] Input: Audio data

[1148] Output: Audio Profile

[1149] Step 6:

[1150] The server converts the voice data into text based on the voice profile, and the generated text data is stored in the server's database.

[1151] Input: Audio Profile

[1152] Output: Text data stored in the database

[1153] Step 7:

[1154] The server sets the parameters of the synthesized voice (sound quality, tone, speed, etc.) and assigns an identification code (e.g., NFT) to the voice data. The identification code is important to prevent fraud and forgery.

[1155] Input: Audio Profile

[1156] Output: Audio data with identification code

[1157] Step 8:

[1158] The server invokes a speech synthesis engine (e.g., a "speech synthesis service") using the configured parameters to generate a synthesized voice. This voice file is stored in the server's database.

[1159] Input: Audio data with parameters set

[1160] Output: Synthesized speech files stored in a database

[1161] Step 9:

[1162] The user taps the "Download generated audio" button, which causes the device to send an HTTP request to the server to retrieve the specific audio data.

[1163] Input: User download request

[1164] Output: HTTP request to the server

[1165] Step 10:

[1166] The server receives the HTTP request and returns the generated synthesized voice data to the terminal.

[1167] Input: HTTP request

[1168] Output: Synthesized voice data sent to the device

[1169] Step 11:

[1170] The device saves the received synthesized voice data locally and prepares it for playback. Specifically, it saves the voice file and prepares it for playback on a media player.

[1171] Input: Synthesized voice data sent from the server

[1172] Output: Locally saved playable audio file

[1173] Through the above steps, the present invention enables efficient recording, analysis, transcription, and reuse of user speech as synthesized speech.

[1174] (Application example 1)

[1175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1176] Conventional voice authentication systems have issues with voice recognition accuracy and security, and are not sufficiently effective in fields requiring advanced authentication. Furthermore, there is a lack of means to prevent unauthorized use of voices that exploit the voice characteristics of individual users. The object of this invention is to provide a highly accurate and secure authentication system that utilizes user voice data, thereby improving the reliability and convenience of voice authentication.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1178] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data and generating a voice profile in the server, means for transcribing based on the voice profile, means for setting parameters for synthetic voice, means for assigning a unique identifier to the voice data, means for generating synthetic voice, means for the user to download the generated voice data, means for performing security authentication using the user's voice, means for storing a voice profile of phrases required for security authentication, and means for performing access control based on the results of voice authentication, thereby enabling highly accurate and secure voice authentication.

[1179] "User voice" refers to the user's vocalizations that are recorded and analyzed by the system.

[1180] A "means for recording" is a function that uses a device such as a microphone to capture the user's voice in digital form.

[1181] "Means of transmission" refers to the technology for transferring recorded data to a server via the Internet or a network.

[1182] The "means for analyzing and generating a voice profile" is a function that uses a voice recognition engine to analyze voice data and compile voice characteristics into a data set.

[1183] "Transcribing means" refers to a technology that converts audio data into text format based on an audio profile.

[1184] The "means for setting parameters of synthesized speech" is a function for setting speech characteristics such as sound quality, tone, and speed.

[1185] "Means for assigning an identifier" refers to a technology for assigning a unique identification code to audio data.

[1186] "Means for generating synthetic voice" refers to technology that generates artificial voice that reproduces the user's voice characteristics based on set parameters.

[1187] The "means for downloading" is a function for importing the generated audio data into the user's device.

[1188] "Means for performing security authentication" refers to technology that uses the user's voice to perform identity authentication.

[1189] A "means for storing voice profiles" is a technology that stores a data set of voice characteristics required for security authentication.

[1190] "Means for access control" refers to technology that manages access permissions to systems and services based on the results of voice authentication.

[1191] The present invention relates to a system for recording, analyzing, and synthesizing a user's voice and making the generated voice data available. A specific method for implementing this system will be described below.

[1192] 1. Recording and sending

[1193] The user records their own voice using a smartphone or computer. The user launches the application and taps the "Start Recording" button to begin recording. The user speaks a specified phrase and taps the "Stop Recording" button when recording is complete. The recorded voice data is temporarily stored in the device's local storage. The device then generates and sends an HTTP request to send the recorded data to the server.

[1194] 2. Audio analysis and profile generation

[1195] Once the server receives the voice data, it analyzes it using a speech recognition engine (for example, Google Cloud Speech-to-Text API or VOSK), which generates a voice profile from the voice data. This voice profile contains the individual characteristics of the voice data and is later used for transcription to text and voice authentication.

[1196] 3. Transcription and parameter setting

[1197] The server transcribes the text based on the voice profile, which converts the audio data into text format. Then, to set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile.

[1198] 4. Identification and synthetic speech generation

[1199] A unique identifier (e.g., hash value or NFT) is assigned to the voice data to prevent unauthorized use or forgery of the data. The server uses the configured parameters to launch a speech synthesis engine (e.g., Amazon Polly or Google Text-to-Speech) to generate synthetic speech. For example, based on the text "Hello, my name is User," a synthetic speech that reproduces the user's voice characteristics is generated. The generated synthetic speech is stored in a database.

[1200] 5. Download and Use

[1201] To use the generated synthesized speech, the user downloads it from the application. When the user taps the "Download generated speech" button, the device sends an HTTP request to the server, requesting specific speech data. The server receives the request and sends the generated synthesized speech data to the device. The device then saves the received speech data locally and makes it playable.

[1202] Specific examples

[1203] As a concrete example, consider voice authentication when a user logs in to a security system. The user launches an application on their smartphone and says, "My name is ____. Please allow me to log in." The recorded voice is sent to the server, which generates a voice profile. The generated voice profile is compared with pre-registered voice characteristics to perform authentication. If authentication is successful, access to the security system is granted.

[1204] Example prompt sentence:

[1205] Simply say the phrase "My name is ____. Please allow me to log in." Your recording will be run through a speech recognition engine and converted into text. A voice profile will then be generated that will be used for security authentication.

[1206] In this way, the present invention provides a highly accurate and safe voice authentication system that effectively utilizes the user's voice.

[1207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1208] Step 1:

[1209] A user starts an application on a smartphone or computer and taps the "Start Recording" button to start recording. The input is the user's voice, and the output is the recorded voice data. The recorded voice data is temporarily stored in the device's local storage.

[1210] Step 2:

[1211] When the user finishes recording, they tap the "Stop Recording" button. The input is the user's operation, and the output is the command to stop recording. The recorded audio data is saved in the device's local storage.

[1212] Step 3:

[1213] The device sends the recorded data to the server. The input is the recorded audio data, and the output is an HTTP request to the server. This request contains the audio data.

[1214] Step 4:

[1215] The server receives the voice data and analyzes it using a speech recognition engine. The input is the voice data sent to the server, and the output is the analyzed voice profile. The speech recognition engine uses Google Cloud Speech-to-Text API or VOSK.

[1216] Step 5:

[1217] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. This data is stored in the server's database.

[1218] Step 6:

[1219] To set the parameters of the synthesized voice, the server sets the necessary parameters (quality, tone, speed, etc.) based on the voice profile. The input is the voice profile, and the output is the set parameters.

[1220] Step 7:

[1221] The server assigns a unique identifier to the audio data. The input is the audio data, and the output is the audio data with the assigned identifier. This identifier is composed of a hash value, NFT, etc.

[1222] Step 8:

[1223] The server uses the configured parameters to start the speech synthesis engine and generate synthetic speech. The input is the configured parameters and text data, and the output is synthetic speech data. The speech synthesis engine used is Amazon Polly or Google Text-to-Speech.

[1224] Step 9:

[1225] The generated synthetic speech is stored in a database on the server. The input is the synthetic speech data, and the output is the synthetic speech data stored in the database.

[1226] Step 10:

[1227] To use the generated synthesized speech, the user taps the "Download generated speech" button in the application. The input is the user's download request, and the output is an HTTP request to the server.

[1228] Step 11:

[1229] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the HTTP request for download, and the output is the synthesized voice data.

[1230] Step 12:

[1231] The device stores the received voice data locally and makes it playable. The input is the synthesized voice data from the server, and the output is the locally stored voice data.

[1232] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1233] This invention relates to a system that records, analyzes, and synthesizes a user's voice and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. Below, we will explain the program processing of this system in natural language and provide a detailed description with specific examples of its use.

[1234] First, the user records their own voice using the device. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. The recorded voice data is temporarily stored in the device's local storage.

[1235] Next, the device sends the recorded audio data to the server. The device generates an HTTP request to send the recorded data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[1236] Once the server receives the audio data, it analyzes it using a speech recognition engine, which generates a voice profile from the audio data, which is a dataset of the audio data's individual characteristics that is used to transcribe it into text.

[1237] The server then transcribes the text based on the voice profile, converting the audio data into text format, and stores the generated text in a database.

[1238] With the introduction of the emotion engine, the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, speed, etc. of the voice and adds that information to the voice profile. The recognized emotion information is used in the process of generating synthetic voice.

[1239] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server sets the tone of the synthesized voice to be brighter and the speed to be faster.

[1240] The server assigns a unique identifier (e.g., NFT) to the audio data to prevent unauthorized use or forgery. The audio data with the assigned identifier is then stored in a database.

[1241] In generating synthetic speech, the server uses the set parameters to start the speech synthesis engine and generate synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[1242] To use the generated voice data, the user can download the generated voice from the application. When the user taps the "Download generated voice" button, the device sends an HTTP request to the server, requesting specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data locally and makes it playable.

[1243] As a concrete example, consider the case where a user suddenly loses their voice during a presentation. In this case, the user can convey their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and have other users purchase them, creating a new source of revenue.

[1244] In this way, the present invention provides a system that effectively utilizes the user's voice, solves various voice problems, enables the generation of natural voice that reflects the user's emotions, and further enables the monetization of voice data.

[1245] The processing flow will be explained below.

[1246] Step 1:

[1247] The user records audio. The user launches the application on their smartphone or computer and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when recording is complete. The recorded audio data is temporarily stored in the device's local storage.

[1248] Step 2:

[1249] The device sends the recorded data to the server. The device generates an HTTP request to send the recorded audio data to the server and sends the request to the server. The server receives this request and retrieves the audio data.

[1250] Step 3:

[1251] The server analyzes the voice data. The server starts a voice recognition engine and analyzes the received voice data. Through the analysis, a voice profile is generated from the voice data. This voice profile is a data set that describes the characteristics of the voice data.

[1252] Step 4:

[1253] The server performs the transcription. Based on the voice profile, the server starts the process of converting the voice data into text. The transcribed text is formatted as "Hello, my name is Taro Tanaka" and stored in a database.

[1254] Step 5:

[1255] The server recognizes emotions. The server uses an emotion engine to analyze the user's emotions from the voice data. The emotion engine analyzes characteristics of the voice, such as tone, rhythm, and speed, to identify the user's emotions and adds the results to the voice profile.

[1256] Step 6:

[1257] The server sets the parameters of the synthesized voice. The server adjusts the parameters of the synthesized voice (sound quality, tone, speed, etc.) based on the voice profile and the results of the emotion engine. For example, if the user expresses joy, the server will set the tone of the synthesized voice to be brighter and the speed to be faster.

[1258] Step 7:

[1259] The server assigns a unique identifier to the audio data. The server uses an identifier generation algorithm to assign a unique identifier such as an NFT to the generated audio data to prevent unauthorized use or counterfeiting of the data. The audio data with the assigned identifier is then stored in a database.

[1260] Step 8:

[1261] The server generates synthetic speech. The server activates a speech synthesis engine based on the set parameters to generate synthetic speech. As a specific example, based on the text "Hello, my name is Tanaka Taro," synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is stored in a database.

[1262] Step 9:

[1263] The user downloads the generated audio data. The user taps the "Download generated audio" button in the application. The device sends an HTTP request to the server to retrieve the requested audio data. The server sends the generated audio data to the device. The device saves the received audio data locally and makes it playable.

[1264] Step 10:

[1265] Users can use the audio data. By playing the generated audio data during presentations or events, users can convey their messages even in situations where they have difficulty speaking. Users can also earn revenue by putting the generated audio data on the marketplace and making it available for purchase by other users.

[1266] Example 2

[1267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1268] Conventional speech synthesis systems have had difficulty generating natural-sounding speech that reflects the user's emotions. Furthermore, proper identifiers were not assigned to voice data to prevent unauthorized use or forgery, and the authenticity of the voice data could not be guaranteed. Furthermore, there was no way for users to easily download and use synthesized voices.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1270] In this invention, the server includes means for recognizing and adding emotional information from voice data using an emotion engine, means for setting parameters for synthetic voice based on the emotional information, and means for assigning a unique identifier to the voice data. This makes it possible to generate natural synthetic voice that reflects the user's emotions and realize an environment in which users can easily use voice data while ensuring high authenticity of the voice data.

[1271] "User" refers to any individual or entity that uses the System.

[1272] "Audio Data" refers to the digital form of audio recorded by a user.

[1273] "Server" refers to the computing resources for receiving, analyzing, storing, and otherwise processing audio data.

[1274] "Audio profile" refers to information about audio characteristics obtained by analyzing audio data.

[1275] "Transcription" refers to the process of analyzing audio data and converting it into text form.

[1276] An "emotion engine" refers to a program that recognizes a user's emotions from voice data and obtains that information.

[1277] "Emotion information" refers to data relating to the user's emotions recognized from the voice data.

[1278] "Synthetic voice" refers to an artificial voice generated based on a voice profile and emotional information.

[1279] "Unique identifier" refers to a unique identification code assigned to voice data to prevent unauthorized use or forgery.

[1280] "Parameters" refer to the setting items used when generating synthetic speech.

[1281] "Database" refers to a system that stores voice data and related information in an organized manner.

[1282] This invention relates to a system that records, analyzes, and synthesizes a user's voice, and makes the generated voice data available. By combining this system with an emotion engine, it can recognize the user's emotions and adjust the parameters of the synthesized voice based on those emotions. The following describes the specific names of each piece of hardware and software, as well as details of data processing and data calculation.

[1283] First, the user records their own voice using the device. The user launches an application on their smartphone or computer (e.g., a dedicated recording app) and taps the "Start Recording" button to begin recording. The user speaks their message and taps the "Stop Recording" button when they are finished recording. At this stage, the recorded voice data is temporarily stored in the device's local storage.

[1284] Next, the device sends the recorded voice data to the server. The device generates an HTTP request and sends the recorded data to the server. The server receives this request and retrieves the voice data. A voice recognition engine (for example, Google Speech-to-Text API or Azure Cognitive Services) is used to analyze the voice data. This generates a voice profile from the voice data and converts it into text format. The generated text is stored in a database on the server.

[1285] Using an emotion engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotion from the voice data. The emotion engine identifies the user's emotion from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. This emotion information is used in the synthetic voice generation process.

[1286] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as voice quality, tone, and speed) based on the emotion recognized by the emotion engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[1287] The server then assigns a unique identifier (such as a unique identification code or an NFT using blockchain technology) to the audio data to prevent unauthorized use or counterfeiting. The audio data with the assigned identifier is then stored in a database.

[1288] To generate synthetic speech, the server uses the set parameters to launch a speech synthesis engine (for example, Google Text-to-Speech API or Amazon Polly) and generates synthetic speech. As a specific example, based on the text "Hello, my name is Taro Tanaka," a synthetic speech that reproduces the user's voice characteristics and emotional characteristics is generated. The generated speech data is then saved back into the database.

[1289] Finally, the user can use the generated voice data. For example, when a user taps the "Download generated voice" button in an application, the device sends an HTTP request to the server to obtain specific voice data. The server receives the request and sends the generated synthesized voice data to the device. The device then saves the received voice data in local storage and makes it playable.

[1290] For example, if a user suddenly loses their voice during a presentation, they can deliver their message using a pre-recorded and generated synthetic voice that reflects their emotions. Users can also offer their voices on a marketplace and earn new revenue by having other users purchase them.

[1291] Examples of prompts include, "If I lose my voice during a presentation, how can I communicate my message using pre-recorded or pre-generated synthesized speech?" and "How can I put my voice on the marketplace so other users can purchase it?"

[1292] This system effectively utilizes the user's voice, solves various voice problems, and enables natural voice generation that reflects the user's emotions. Furthermore, it also allows for monetization of voice data.

[1293] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1294] Step 1:

[1295] The user launches the application on their device and taps the "Start Recording" button.

[1296] Input: User operation (tapping the start recording button)

[1297] Output: Start of the recording process

[1298] Specific operation: The user launches the app on their smartphone, goes to the recording screen, and presses the start recording button. The microphone is enabled and audio begins to be recorded.

[1299] Step 2:

[1300] The user speaks their message and taps the "Stop Recording" button.

[1301] Input: User's voice, tapping the stop recording button

[1302] Output: Recorded audio data

[1303] Specific operation: When the user finishes speaking, they press the stop recording button. The recording will stop and the audio data will be saved in the device's local storage.

[1304] Step 3:

[1305] The device generates an HTTP request to send the recorded audio data to the server.

[1306] Input: Recorded audio data

[1307] Output: HTTP request

[1308] Specific behavior: The app generates an HTTP request containing the recording data and metadata (recording date and time, user ID, etc.).

[1309] Step 4:

[1310] The device sends an HTTP request to the server.

[1311] Input: HTTP request

[1312] Output: Send data to the server

[1313] What happens: Your device sends audio data to a server using a Wi-Fi or mobile data connection.

[1314] Step 5:

[1315] The server receives the HTTP request and retrieves the audio data.

[1316] Input: HTTP request

[1317] Output: Save audio data

[1318] Specific operation: The server receives the request at the API endpoint and saves the audio data to storage.

[1319] Step 6:

[1320] The server uses a speech recognition engine to analyze the voice data and convert it into text format.

[1321] Input: Audio data

[1322] Output: Audio profile and text data

[1323] Specific operation: Calls a speech recognition engine (e.g., Google Speech-to-Text API), analyzes the voice data, converts it into text, and saves the generated text in a database.

[1324] Step 7:

[1325] The server uses an emotion engine to recognize the user's emotion from the voice data and adds the emotion information to the voice profile.

[1326] Input: Audio data, text data

[1327] Output: Voice profile with added emotional information

[1328] What it does: An emotion engine (e.g., IBM Watson Tone Analyzer) analyzes tone, rhythm, and speed to identify the user's emotion and add it to the voice profile.

[1329] Step 8:

[1330] The server sets the parameters of the synthesized voice based on the emotion information.

[1331] Input: A voice profile with added emotional information

[1332] Output: Synthesized voice parameters

[1333] Specific operation: If the emotion is "joy," the parameters of the synthesized voice are set, such as making the tone brighter and the speed faster.

[1334] Step 9:

[1335] The server assigns a unique identifier to the audio data.

[1336] Input: Audio data

[1337] Output: Audio data with identifiers

[1338] Specific operation: Generate a unique identification code (e.g., NFT) and embed it in the audio data. Store the audio data with the identifier in a database.

[1339] Step 10:

[1340] The server starts the speech synthesis engine and generates synthetic speech using the configured parameters.

[1341] Input: Synthesized voice parameters, text data

[1342] Output: Synthesized voice data

[1343] Specific operation: Calls a speech synthesis engine (e.g., Google Text-to-Speech API) and generates synthetic speech using the text and voice profile as input. The generated speech data is saved in a database.

[1344] Step 11:

[1345] The user downloads the generated audio data.

[1346] Input: User download request

[1347] Output: Audio data sent to the device

[1348] Specific operation: When a user taps the "Download generated audio" button in the application, the device sends an HTTP request to the server, requesting specific audio data. The server receives the request and sends the generated synthesized audio data to the device. The device then saves the received audio data in local storage and makes it playable.

[1349] In this way, each processing step involves a specific operation, and the system functions with the roles of the user, terminal, and server allotted according to their roles.

[1350] (Application example 2)

[1351] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1352] When a user uses a food delivery service, it is difficult for customer support to quickly and accurately determine the user's emotions and respond appropriately to those emotions. In particular, when a customer is dissatisfied or encounters a problem, it is important to properly understand the customer's emotions and respond accordingly to improve service satisfaction. This invention aims to realize a natural voice response that reflects the user's emotions and improves the user experience.

[1353] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1354] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice to the server, means for analyzing the voice data in the server and generating a voice profile, means for recognizing the user's emotion from the voice data using an emotion recognition engine, means for setting parameters for synthetic voice based on the recognized emotion information, means for assigning a unique identifier to the voice data, means for generating synthetic voice, and means for the user to download the generated voice data. This enables a response using synthetic voice that reflects the user's emotion, thereby improving satisfaction with the food delivery service.

[1355] "Means for recording the user's voice" refers to equipment or software that records the user's voice as digital data.

[1356] "Means for transmitting recorded audio to a server" refers to a function that transfers recorded audio data to a remote server using the Internet or other communication methods.

[1357] "Means for analyzing voice data on the server and generating a voice profile" refers to a function for analyzing voice data sent to the server and generating a voice profile that indicates the individual characteristics of the data.

[1358] "Means for transcribing based on an audio profile" refers to a function for converting audio data into text data based on an audio profile.

[1359] "Means for recognizing a user's emotions from voice data using an emotion recognition engine" refers to a function that analyzes elements such as tone and rhythm from voice data and identifies the user's emotions.

[1360] "Means for setting parameters of synthesized voice based on recognized emotional information" refers to a function for adjusting settings such as tone and speed of synthesized voice based on recognized emotional information.

[1361] "Means for assigning a unique identifier to audio data" refers to a function that assigns a unique identifier to audio data to prevent unauthorized use or forgery of the data.

[1362] The "means for generating synthetic speech" refers to a function that activates a speech synthesis engine using set parameters to generate natural-sounding speech.

[1363] "Means for users to download generated voice data" refers to a function that transmits the generated synthesized voice data from the server to the user's terminal and makes it available for download.

[1364] The present invention provides a system for providing utterances that reflect the user's emotions in customer support for food delivery services. The system can be implemented using a variety of hardware and software.

[1365] First, the user records audio using a smartphone. Launch the application on the smartphone, tap the "Start Recording" button to begin recording, and speak the message. When recording is finished, tap the "Stop Recording" button. The recorded audio data is temporarily stored in the smartphone's local storage.

[1366] Next, the smartphone sends the recorded data to the server. The smartphone generates an HTTP request and sends it to the server. The server receives the request and retrieves the audio data.

[1367] Once the server receives the audio data, it analyzes it using a speech recognition engine (e.g., Google Speech-to-Text API), which generates a voice profile from the audio data. This voice profile is a dataset of the audio data's individual characteristics, which is used to transcribe it into text.

[1368] The server then transcribes the text based on the voice profile, converting the voice data into text format. The server stores the generated text in a database. Using an emotion recognition engine (e.g., IBM Watson Tone Analyzer), the server recognizes the user's emotions from the voice data. The emotion engine identifies the user's emotions from the tone, rhythm, and speed of the voice, and adds this information to the voice profile. The recognized emotion information is used in the synthetic voice generation process.

[1369] When setting the parameters of the synthesized voice, the server adjusts the necessary parameters (such as tone, speed, etc.) based on the emotion recognized by the emotion recognition engine. For example, if the user expresses joy, the server adjusts the tone of the synthesized voice to be brighter and the speed to be faster.

[1370] The server assigns a unique identifier to the voice data. The voice data with the assigned identifier is stored in a database. To generate synthetic voice, the server uses the configured parameters to launch a voice synthesis engine (e.g., Amazon Polly) and generates synthetic voice. The generated voice data is stored in a database.

[1371] To use the generated voice data, the user downloads the generated voice from the smartphone application. When the user taps the "Download generated voice" button, the smartphone sends an HTTP request to the server, requesting the acquisition of specific voice data. The server receives the request and sends the generated synthesized voice data to the smartphone. The smartphone then stores the received voice data locally and makes it playable.

[1372] As a concrete example, consider when a user reports a problem with a food delivery service to telephone support. At this time, the user can pre-record utterances that reflect their emotions and convey their feelings appropriately. Furthermore, if the customer support understands the user's emotions and responds appropriately to the situation, customer satisfaction with food delivery services can be significantly improved.

[1373] Example prompts to input to a generative AI model:

[1374] Analyze the user's voice recording, recognize emotions based on the tone, rhythm, and rate of the voice, and adjust the tone and rate of the synthesized voice accordingly, such as making the tone brighter and faster if the user is expressing joy.

[1375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1376] Step 1:

[1377] The user starts the smartphone app and taps the "Start Recording" button to begin recording their voice. When the user has finished speaking, they tap the "Stop Recording" button to stop recording. This operation saves the user's speech as digital data in local storage. The input is the user's voice, and the output is voice data.

[1378] Step 2:

[1379] The device acquires the recorded voice data, generates an HTTP request, and sends it to the server. The input is voice data, and the output is an HTTP request. Specifically, the device converts the voice data into binary format and sends it to the server.

[1380] Step 3:

[1381] The server receives the HTTP request and extracts the audio data. It then analyzes the audio data using a speech recognition engine (e.g., Google Speech-to-Text API) and generates a voice profile. The input is the audio data and the output is the voice profile. The server converts the audio file to text and stores the text in a database.

[1382] Step 4:

[1383] The server transcribes the text based on the voice profile. The input is the voice profile and the output is text data. Specifically, the voice recognition engine analyzes the features obtained from the voice data and converts it into text data.

[1384] Step 5:

[1385] The server uses an emotion recognition engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions from text data. The input is text data, and the output is emotional information. Specifically, the server analyzes the tone, rhythm, and speed of the text and assigns an emotional label (e.g., joy, sadness, anger).

[1386] Step 6:

[1387] The server sets the parameters of the synthesized speech (sound quality, tone, speed, etc.) based on the emotional information. The input is the emotional information, and the output is the parameters of the synthesized speech. Specifically, the characteristics of the synthesized speech are dynamically adjusted according to the emotional information.

[1388] Step 7:

[1389] The server assigns a unique identifier to the audio data. The input is audio data, and the output is audio data with an identifier. Specifically, a unique identifier is generated using an identifier generation algorithm and added to the audio data.

[1390] Step 8:

[1391] The server uses a speech synthesis engine (e.g., Amazon Polly) to generate synthetic speech based on the set parameters. The input is the set parameters, and the output is the synthesized speech data. Specifically, the speech synthesis engine takes text and parameters as input and generates natural-sounding speech.

[1392] Step 9:

[1393] When a user taps the "Download generated voice" button on the smartphone app, the device sends an HTTP request to the server to request specific synthesized voice data. The input is the user request, and the output is an HTTP request.

[1394] Step 10:

[1395] The server receives the request and sends the generated synthesized voice data to the terminal. The input is the user request and the output is the synthesized voice data. Specifically, the server searches for the voice data stored and sends the corresponding voice data to the terminal.

[1396] Step 11:

[1397] The device stores the received synthesized speech data locally and makes it playable. The input is synthesized speech data, and the output is playable speech data. Specifically, the device stores the data in an appropriate format and plays it back on an audio player or similar device so that the user can listen to it.

[1398] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1399] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1400] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1401] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1402] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1403] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1404] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1405] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1406] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1407] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1408] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1409] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1410] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1411] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1412] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1413] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1414] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1415] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1416] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1417] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1418] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1419] The following is further disclosed regarding the above embodiment.

[1420] (Claim 1)

[1421] means for recording the user's voice;

[1422] a means for transmitting the recorded audio to a server;

[1423] means for analyzing the voice data at a server and generating a voice profile;

[1424] a means for generating a transcription based on the audio profile;

[1425] a means for setting parameters of the synthesized speech;

[1426] a means for assigning a unique identifier to the audio data;

[1427] means for generating synthetic speech;

[1428] The system includes means for a user to download the generated audio data.

[1429] (Claim 2)

[1430] 10. The system of claim 1, further comprising means for using a speech recognition engine in analyzing the speech data.

[1431] (Claim 3)

[1432] 2. The system according to claim 1, further comprising means for the server to use an identifier generation algorithm when assigning an identifier to the audio data.

[1433] "Example 1"

[1434] (Claim 1)

[1435] means for recording the user's voice;

[1436] means for transmitting the recorded voice to an information processing device;

[1437] means for analyzing voice data and generating a voice profile in an information processing device;

[1438] a means for generating a transcription based on the audio profile;

[1439] a means for setting parameters of the synthesized speech;

[1440] means for assigning an identification code to the voice data;

[1441] means for generating synthetic speech;

[1442] The system includes means for a user to download the generated audio data.

[1443] (Claim 2)

[1444] 10. The system of claim 1, further comprising means for using a speech recognition engine in analyzing the speech data.

[1445] (Claim 3)

[1446] 2. The system according to claim 1, further comprising means for using an identification code generation algorithm when the information processing device assigns an identification code to the voice data.

[1447] "Application Example 1"

[1448] (Claim 1)

[1449] means for recording the user's voice;

[1450] a means for transmitting the recorded audio to a server;

[1451] means for analyzing the voice data at a server and generating a voice profile;

[1452] a means for generating a transcription based on the audio profile;

[1453] a means for setting parameters of the synthesized speech;

[1454] a means for assigning a unique identifier to the audio data;

[1455] means for generating synthetic speech;

[1456] A means for a user to download the generated audio data;

[1457] means for performing security authentication using a user's voice;

[1458] means for storing an audio profile of a phrase required for security authentication;

[1459] means for performing access control based on the result of voice authentication;

[1460] A system including:

[1461] (Claim 2)

[1462] 10. The system of claim 1, further comprising means for using a speech recognition engine in analyzing the speech data.

[1463] (Claim 3)

[1464] 2. The system according to claim 1, further comprising means for the server to use an identifier generation algorithm when assigning an identifier to the audio data.

[1465] "Example 2: Combining Emotion Engines"

[1466] (Claim 1)

[1467] means for recording the user's voice;

[1468] a means for transmitting the recorded audio to a server;

[1469] means for analyzing the voice data at a server and generating a voice profile;

[1470] a means for generating a transcription based on the audio profile;

[1471] means for recognizing and adding emotional information from the audio data using an emotion engine;

[1472] means for setting parameters of the synthesized speech based on emotion information;

[1473] a means for assigning a unique identifier to the audio data;

[1474] means for generating synthetic speech;

[1475] The system includes means for a user to download the generated audio data.

[1476] (Claim 2)

[1477] 10. The system of claim 1, further comprising means for using a speech recognition engine in analyzing the speech data.

[1478] (Claim 3)

[1479] 2. The system according to claim 1, further comprising means for the server to use an identifier generation algorithm when assigning an identifier to the audio data.

[1480] "Application example 2 when combining emotion engines"

[1481] (Claim 1)

[1482] means for recording the user's voice;

[1483] a means for transmitting the recorded audio to a server;

[1484] means for analyzing the voice data at a server and generating a voice profile;

[1485] a means for generating a transcription based on the audio profile;

[1486] means for recognizing a user's emotion from the voice data using an emotion recognition engine;

[1487] means for setting parameters of a synthetic speech based on the recognized emotion information;

[1488] a means for assigning a unique identifier to the audio data;

[1489] means for generating synthetic speech;

[1490] The system includes means for a user to download the generated audio data.

[1491] (Claim 2)

[1492] 10. The system of claim 1, further comprising means for using a speech recognition engine in analyzing the speech data.

[1493] (Claim 3)

[1494] 2. The system according to claim 1, further comprising means for the server to use an identifier generation algorithm when assigning an identifier to the audio data. [Explanation of symbols]

[1495] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for recording the user's voice; a means for transmitting the recorded audio to a server; means for analyzing the voice data at a server and generating a voice profile; a means for generating a transcription based on the audio profile; a means for setting parameters of the synthesized speech; a means for assigning a unique identifier to the audio data; means for generating synthetic speech; The system includes means for a user to download the generated audio data.

2. 10. The system of claim 1, further comprising means for using a speech recognition engine in analyzing the speech data.

3. 2. The system according to claim 1, further comprising means for using an identifier generation algorithm when the server assigns an identifier to the audio data.

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