System

Abstract

A system is provided.SOLUTION: A system, comprising: means for recording a user's speech; means for sending the recorded speech data to a server; means for inputting pre-processed speech data into a generative model and converting it into a different speech; means for sending the converted speech data to the user's terminal; and means for playing the received converted speech 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] Providing a service that allows people interested in audio content and musical instrument performances to easily convert their own voices and enjoy them requires advanced technology and specialized knowledge, and there are few easily accessible services that meet these requirements. Furthermore, sufficient consideration must be given to the security and privacy of voice data. By resolving these issues, we aim to provide a voice conversion service that users can use with peace of mind. [Means for solving the problem]

[0005] The system includes a means for recording a user's voice and a means for transmitting the recorded voice data to a server, and a means for inputting the preprocessed voice data into a generative model in the server to convert it into a different voice. It also includes a means for transmitting the converted voice data to the user's terminal and a means for playing the received converted voice data. It also includes a preprocessing means for performing noise reduction and volume normalization on the recorded voice data, and a means for converting the voice based on a voice profile selected by the generative model. It also includes a means for selecting the voice profile from multiple types in advance, and a communication protocol and storage using encryption technology to ensure data security.

[0006] A "user" is an entity that uses this system to record and convert their own voice.

[0007] "Voice Data" means information that is a digital representation of a User's recorded voice.

[0008] A "server" is a computer system that processes, converts, and responds to voice data.

[0009] A "generative model" is an artificial intelligence model for converting input voice data into another specified voice.

[0010] "Preprocessing" refers to the process of processing audio data, such as noise removal and volume normalization, to make it easier for the generative model to process.

[0011] A "voice profile" is data that defines the characteristics of the target voice used by the generative model.

[0012] "Terminal" means a device used by a User that has the ability to record and play audio.

[0013] "Noise reduction" is a process that removes unnecessary background sounds and noise from audio data.

[0014] "Volume normalization" is the process of adjusting the overall volume level of audio data to achieve a consistent volume.

[0015] "Data security" refers to a system that safely stores voice data and prevents unauthorized access or tampering by third parties when sending or receiving data.

[0016] A "communication protocol" is a technology that defines the rules and procedures for sending and receiving audio data.

[0017] "Encryption technology" is a technology that encrypts the audio data being sent and received, preventing unauthorized persons from accessing the data. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] System Overview

[0040] The system is a service that allows users to record their own voice and convert it into a different voice, and consists of the following components:

[0041] A means of recording the user's voice

[0042] A means of sending recorded audio data to a server

[0043] A means to preprocess speech data on the server and input it into a generative model

[0044] A means of returning the audio converted by the generative model to the user

[0045] A means to play the converted audio

[0046] A means of recording the user's voice

[0047] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved on the device.

[0048] A means of sending recorded audio data to a server

[0049] Once the recording is complete, the user selects the voice profile they want to convert (e.g., the voice of the opposite sex or a specific voice actor). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[0050] A means to preprocess speech data on the server and input it into a generative model

[0051] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[0052] A means of converting audio using a generative model

[0053] A generative model converts input audio data into a specified voice based on a pre-trained voice profile, for example, converting a male voice into a female voice or changing the voice of a specific character.

[0054] A means of returning the converted audio to the user

[0055] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[0056] A means to play the converted audio

[0057] Once the user's device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[0058] Specific examples

[0059] For example, consider a case where a user records "Good morning" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "Good morning" is played in the anime character's voice.

[0060] By using this system, users can easily convert their own voice into a variety of voices, allowing them to experience new forms of enjoyment in communication and entertainment settings.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user launches the application on their device and presses the record button to record their voice.

[0064] Step 2:

[0065] The device temporarily stores the recorded audio data in memory.

[0066] Step 3:

[0067] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[0068] Step 4:

[0069] The device packages the selected audio profile and the recorded audio data and sends it to the server.

[0070] Step 5:

[0071] The server receives the voice data package transmitted from the terminal.

[0072] Step 6:

[0073] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[0074] Step 7:

[0075] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[0076] Step 8:

[0077] The server performs a volume normalization process to adjust the volume level of the audio to a consistent standard.

[0078] Step 9:

[0079] The server prepares the preprocessed audio data to be input into the generative model.

[0080] Step 10:

[0081] A generative model analyzes the preprocessed audio data and transforms the audio based on a specified audio profile.

[0082] Step 11:

[0083] The generative model generates the converted audio data and returns it to the server.

[0084] Step 12:

[0085] The server stores the generated converted voice data in temporary storage.

[0086] Step 13:

[0087] The server then sends the converted audio data to the user's device using encryption technology.

[0088] Step 14:

[0089] The terminal receives the converted voice data from the server and stores it in temporary memory.

[0090] Step 15:

[0091] The user presses the application's play button to play the converted audio data.

[0092] Step 16:

[0093] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[0094] This processing flow allows users to easily convert their recorded voice into a different voice and see the results in real time.

[0095] Example 1

[0096] 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."

[0097] In today's world, there is a growing need for users to convert their own voice into that of another person or a specific character. However, conventional systems often lack the accuracy and processing speed of voice conversion, resulting in a poor user experience. Furthermore, data security is often not fully considered, creating privacy issues.

[0098] 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.

[0099] In this invention, the server includes a means for recording the user's voice, a means for transmitting the recorded voice data to the server, a means for preprocessing the voice data on the server and inputting the voice into a generative model to convert it into a different voice, a means for transmitting the converted voice data to the user's device, and a means for playing back the received converted voice data. This achieves high conversion accuracy and processing speed, allowing the user to experience fast and accurate voice conversion. In addition, the use of encryption technology ensures data security and protects the user's privacy.

[0100] "Means for recording the user's voice" refers to the means by which a user records voice through a microphone using a device such as a smartphone or computer.

[0101] The "means for transmitting recorded voice data to the server" refers to a means for transmitting voice data and related information from the user's terminal to the server.

[0102] "Means for preprocessing audio data on a server and inputting the audio into a generative model to convert it into a different audio" refers to a means for a server to receive audio data, perform preprocessing such as noise removal and volume normalization, and then use a generative AI model to convert the audio into a different specified audio.

[0103] The "means for transmitting converted voice data to the user's terminal" refers to a means for transmitting converted voice data from the server to the user's terminal.

[0104] The "means for reproducing the received converted voice data" refers to a means for reproducing the received voice data on the user's terminal.

[0105] "Denoising" is the process of removing unwanted background noise from audio data.

[0106] "Volume normalization" is the process of adjusting the volume level of audio data to a uniform level.

[0107] A "generative model" is an artificial intelligence model for converting speech into another speech based on a learned profile.

[0108] A "voice profile" is a set of pre-defined voice characteristic parameters that determine the characteristics of the converted voice.

[0109] "Encryption technology" is a technology for ensuring data security in communication protocols and storage.

[0110] System Overview

[0111] The system provides technology that allows users to record their own voice and convert it into a different voice. The system includes the following elements:

[0112] A means of recording the user's voice

[0113] A means of sending recorded audio data to a server

[0114] A means to preprocess speech data on the server and input it into a generative model

[0115] A means of converting speech using a generative model

[0116] A means of transmitting the converted audio data to the user's device

[0117] A means of playing the converted audio data

[0118] A means of recording the user's voice

[0119] Users use a dedicated application on their smartphone, PC, or other device to record their own voice using the microphone. Pressing the record button starts recording, and pressing the stop button saves the audio data on the device. For example, if a user says "Good morning," this voice will be recorded.

[0120] A means of sending recorded audio data to a server

[0121] Once the recording is complete, the user selects the voice profile they want to convert within the application, such as "female voice" or "anime character voice." The device then sends the recorded voice data and information about the selected voice profile to the server. The communication is encrypted using the HTTPS protocol.

[0122] A means to preprocess speech data on the server and input it into a generative model

[0123] When the server receives the audio data, it first performs preprocessing such as noise removal and volume normalization. This makes the audio data easier for the generative model to process. For example, it filters out background noise and adjusts volume peaks. The preprocessed audio data is then input into the generative model.

[0124] A means of converting audio using a generative model

[0125] The server inputs the preprocessed voice data into a generative AI model, which converts the voice based on the specified voice profile. For example, to convert a male voice into a female voice, it adjusts the frequency and timbre. The generative model uses a neural network, enabling highly accurate voice conversion.

[0126] A means of transmitting the converted audio data to the user's device

[0127] The converted audio data is temporarily stored on the server and then promptly sent to the user's device, also encrypted using the HTTPS protocol.

[0128] A means to play the converted audio

[0129] When the user's device receives the converted voice data, a play button will appear in the dedicated application. When the user presses the play button, the converted voice will be played and the user can check the results. For example, "Good morning" will be played in the voice of an anime character.

[0130] Examples of concrete examples and prompts

[0131] For example, consider a case where a user records "hello" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "hello" is played in the anime character's voice.

[0132] Examples of prompts used in this system include "Please convert this voice into a female voice" and "Please convert the recorded voice into the voice of an anime character."

[0133] With the above-described configuration and procedures, this system is able to convert a user's voice into another voice with high accuracy and provide it quickly and safely.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1: Record the user's voice

[0136] The user launches a dedicated application and records their own voice using the microphone on their smartphone or computer. When the user taps the record button, voice recording begins, and the voice data up until the end of the recording is saved in the device's temporary memory. When the user taps the stop button, recording ends, and the voice data is saved in the device's storage. The user's voice is given as input, and the recorded voice data is saved as output. For example, the user might say, "Good morning."

[0137] Step 2: Send the recorded audio data to the server

[0138] Once the recording is complete, the user selects the voice profile they want to convert to within the application, for example, "female voice" or "anime character voice." After that, by tapping the send button, the device will send the recorded voice data and the selected voice profile information to the server, encrypting it using the HTTPS protocol. The recorded voice data and the selected voice profile are given as input, and sent to the server as output.

[0139] Step 3: Preprocess the audio data on the server

[0140] When the server receives the audio data, it performs preprocessing such as noise removal and volume normalization. Specifically, it filters out background noise and evens out volume peaks. Recorded audio data is given as input, and preprocessed audio data is generated as output. This converts the data into a format that is easy for the generative AI model to process.

[0141] Step 4: Input audio to the generative AI model and perform conversion

[0142] The preprocessed voice data is input into a generative AI model, which converts it into a different voice based on a specified voice profile. The generative AI model utilizes a trained neural network to convert a male voice into a female voice, or into the voice of a specific character. The preprocessed voice data and voice profile are given as input, and the converted voice data is generated as output. For example, "Good morning" is converted into a female voice.

[0143] Step 5: Send the converted audio data to the user's device

[0144] The converted voice data is temporarily stored on the server and then encrypted using the HTTPS protocol before being sent to the user's device. The converted voice data is given as input and sent to the user's device as output.

[0145] Step 6: Play the converted audio

[0146] When the user's device receives the converted voice data, a play button is displayed within the dedicated application. When the user presses the play button, the converted voice is played. The converted voice data is given as input, and the voice is played as output. For example, when the user presses the play button, "Good morning" is played in the voice of an anime character.

[0147] Through the above processing steps, users can convert their own voice with high accuracy based on various profiles and enjoy it quickly and safely.

[0148] (Application example 1)

[0149] 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."

[0150] Industrial facilities require guidance in multiple languages ​​or with specific character voices, but current systems only support one language and have limited guidance methods. Furthermore, users have limited means of understanding the progress of the process of converting recorded voices into different voices, making efficient communication difficult. Furthermore, robots that provide guidance in industrial facilities require advanced technology to convert voices in real time, but current technology makes this difficult.

[0151] 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.

[0152] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for transmitting the converted voice data to the user's terminal, means for playing back the received converted voice data, means for converting the voice in the robot using a specific voice profile to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and means for displaying the processing status until the recorded voice data is converted. This makes it possible to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and allows users (e.g., visitors or workers) to understand the progress of the voice conversion in real time, thereby achieving efficient communication.

[0153] "Means for recording the user's voice" means means including a microphone or recording device that a user uses to record their voice.

[0154] The "means for transmitting recorded voice data to a server" refers to a means for sending recorded voice data to a server via a network.

[0155] "The effort of inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a method of inputting speech data that has been preprocessed, such as by noise removal and volume normalization, into a generative model and converting it into a specified different speech.

[0156] The "means for transmitting converted voice data to a user's terminal" refers to a means for transmitting voice data converted by a generative model to a user's terminal via a network.

[0157] The "means for reproducing the received converted voice data" refers to a means for reproducing the converted voice data received at the user's terminal.

[0158] "Means for converting voice using a specific voice profile and providing guidance or instructions within an industrial facility in multiple languages ​​or in the voice of a specific character" refers to means for converting voice using a specific voice profile and providing information in different languages ​​or in the voice of a specific character when providing guidance or instructions within an industrial facility.

[0159] The "means for displaying the processing status until the recorded audio data is converted" is a means for displaying to the user the progress status until the conversion process is completed.

[0160] The system for implementing this invention records the user's voice, converts it into a different voice, and provides guidance and instructions within an industrial facility. The overall configuration and operation of the system will be described in detail below.

[0161] System Overview

[0162] The system consists of the following components:

[0163] How to record user voice: The user can record their own voice using the microphone installed on the robot. Recording starts when the start button is pressed, and ends when the stop button is pressed.

[0164] A method for transmitting recorded audio data to a server: Once recording is complete, the audio data is transmitted via wireless communication to a server, which receives the data securely using Internet protocols (e.g., HTTPS).

[0165] A means of inputting preprocessed audio data into a generative model: The server performs preprocessing (noise removal and volume normalization) on the received audio data. The preprocessed data is input into a generative AI model, where it is converted based on a specified audio profile. An example of this generative AI model is Hugging Face's Wav2Vec2.

[0166] A method for sending the converted voice data to the user's terminal: The converted voice data is sent from the server to the robot, which receives the voice data, checks it, and plays it back.

[0167] A means for playing the received converted voice data: The robot is equipped with a speaker and plays the converted voice, allowing it to provide guidance to users and visitors in multiple languages ​​or with the voices of different characters.

[0168] A means of displaying the processing status while the recorded audio data is being converted: The user can check the progress of the recording and conversion on the robot's display or in the mobile application.

[0169] Examples of technologies used

[0170] Hardware: A robot with a microphone for recording audio, a speaker for playing audio, and a network interface to support wireless communication.

[0171] Software: The application that controls the audio recording and playback uses the sounddevice library. For audio conversion, we use a generative AI model (e.g., Hugging Face's Wav2Vec2). The entire data processing and communication uses the secure HTTP communication protocol (HTTPS).

[0172] Specific examples

[0173] For example, consider a scenario where a guide robot in a factory needs to guide visitors, saying "Hello, please come this way." The user records this message through a microphone and selects a specific voice profile (e.g., a female Japanese voice). Once the recording is complete, the audio data is sent to a server where it is preprocessed and converted by a generative AI model. The converted audio data is then sent back to the robot and played back to the visitor.

[0174] Example prompt sentence:

[0175] "In: Hello, please come in

[0176] Out: Japanese female voice

[0177] These methods allow guidance and instructions within industrial facilities to be given in multiple languages ​​or in the voice of a specific character, thereby achieving efficient communication.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] Recording user voice

[0181] When the user presses the recording start button on the robot, the robot's microphone records the voice. The user's voice signal is picked up through the microphone and stored in the device as digital voice data. At this time, the input is the user's voice, and the output is digitized voice data.

[0182] Step 2:

[0183] Sending recording data to the server

[0184] When the user stops recording, the recorded audio data is sent to the server via wireless communication. The HTTPS protocol is used for transmission, ensuring data security. The input here is the recorded audio data, and the output is the data sent to the server.

[0185] Step 3:

[0186] Preprocessing by the server

[0187] The server performs preprocessing on the received audio data. Specifically, it removes noise and normalizes the volume to make the data easier for the generative model to process. The input is the transmitted audio data, and the output is the preprocessed audio data.

[0188] Step 4:

[0189] Voice conversion using generative AI models

[0190] The preprocessed voice data is input to a generative AI model on the server (e.g., Hugging Face's Wav2Vec2), and the voice is converted based on a specified voice profile (e.g., Japanese female voice). An example of a prompt sentence is "In: Hello, please come this way, Out: Japanese female voice." The input is the preprocessed voice data and the voice profile, and the output is the converted voice data.

[0191] Step 5:

[0192] Sending and receiving converted data

[0193] The converted voice data is sent from the server to the robot. The robot's terminal stores the received data and prepares it for playback. The input is the converted voice data, and the output is the data sent to the robot.

[0194] Step 6:

[0195] Playing audio data

[0196] The robot receives the converted data and plays it back through the speaker, allowing users and visitors to listen to the converted audio in real time. The input is the received converted audio data, and the output is the audio that is played back.

[0197] Step 7:

[0198] Viewing Processing Status

[0199] During the conversion process of the voice data, the processing status is displayed on the robot's display and in the mobile application. The user can check the progress. The input is the processing status information, and the output is the status display.

[0200] 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.

[0201] System Overview

[0202] The system is a service that records a user's voice and converts it into a different voice, and also incorporates an emotion engine that recognizes the user's emotions and reflects them in the converted voice. It consists of the following components:

[0203] A means of recording the user's voice

[0204] A means of sending recorded audio data to a server

[0205] Preprocessing method for noise reduction and volume normalization of audio data

[0206] A means of inputting preprocessed speech data into a generative model to convert it into a different speech.

[0207] A means of using an emotion engine to recognize a user's emotional state and select or correct a voice profile based on that

[0208] A means of transmitting the converted audio data to the user's device

[0209] A means for playing the received converted audio data

[0210] A means of recording the user's voice

[0211] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved in the device's temporary memory.

[0212] A means of sending recorded audio data to a server

[0213] Once the recording is complete, the user selects the voice profile they want to convert (for example, the voice of a specific character). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[0214] Audio data preprocessing

[0215] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[0216] Emotion recognition by emotion engine

[0217] The server is equipped with an emotion engine that analyzes the user's emotional state from the recorded voice data. Based on this analysis, the voice profile used by the generative model is automatically selected or corrected, enabling more appropriate voice conversion according to the user's emotions.

[0218] Audio conversion processing

[0219] The generative model converts input speech data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic one to reflect that emotion.

[0220] Transmit and play converted audio

[0221] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[0222] When the device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[0223] Specific examples

[0224] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state based on the user's voice. If the user selects the voice of an animated character, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative model then converts the voice based on that profile, and the converted voice is sent to the user's device via the server. As a result, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[0225] By using this system, users can easily convert their own voice into a variety of voices and even convert the voice according to emotions, providing a richer communication and entertainment experience.

[0226] The processing flow will be explained below.

[0227] Step 1:

[0228] The user launches the application on their device and presses the record button to record their voice.

[0229] Step 2:

[0230] The device temporarily stores the recorded audio data in memory.

[0231] Step 3:

[0232] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[0233] Step 4:

[0234] The device sends the recorded audio data and information about the selected audio profile to the server.

[0235] Step 5:

[0236] The server receives the voice data package transmitted from the terminal.

[0237] Step 6:

[0238] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[0239] Step 7:

[0240] The server's emotion engine analyzes the recorded voice data and recognizes the user's emotional state.

[0241] Step 8:

[0242] The server automatically adjusts or selects the voice profile used by the generative model based on the user's emotional state.

[0243] Step 9:

[0244] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[0245] Step 10:

[0246] The server performs volume normalization, adjusting the volume level of the recorded audio data to a consistent standard level.

[0247] Step 11:

[0248] The server prepares the preprocessed audio data to be input into the generative model.

[0249] Step 12:

[0250] The generative model analyzes the preprocessed speech data and converts it into a specified alternative voice based on information provided by the emotion engine.

[0251] Step 13:

[0252] The generative model generates the converted audio data and returns it to the server.

[0253] Step 14:

[0254] The server stores the generated converted voice data in temporary storage.

[0255] Step 15:

[0256] The server then sends the converted audio data to the user's device using encryption technology.

[0257] Step 16:

[0258] The terminal receives the converted voice data from the server and stores it in temporary memory.

[0259] Step 17:

[0260] The user presses the application's play button to play the converted audio data.

[0261] Step 18:

[0262] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[0263] This process allows users to convert their recorded voice into different voices depending on their emotions and see the results in real time. For example, if the user is feeling "happy," the converted voice will reflect that emotion and be created with a happier tone.

[0264] Example 2

[0265] 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."

[0266] Conventional voice conversion systems have difficulty in converting voices that reflect the user's emotions, making it difficult to generate appropriate voices according to emotions. Ensuring the security of voice data and the stability of the voice conversion process are also issues. To solve these problems, a system with the ability to analyze the user's emotions and automatically select or correct a voice profile according to those emotions is needed.

[0267] 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.

[0268] In this invention, the server includes a means for analyzing the user's emotional state and automatically selecting or correcting a voice profile based on the analysis, a preprocessing means for performing noise reduction and volume normalization on recorded voice data, and a means for requesting a generative AI model to convert input voice data into a different voice based on a pre-trained voice profile. This enables voice conversion according to the user's emotions, resulting in more natural and appropriate communication. Data security is also ensured by the communication protocol and data encryption in the storage device.

[0269] "User" refers to the person who uses the system to record audio and play back the converted audio.

[0270] "Terminal" refers to an electronic device (such as a smartphone or PC) that is owned by the user and that records audio and communicates with the server.

[0271] "Server" refers to a computer system that performs preprocessing of voice data sent from a terminal, emotion analysis, voice profile selection, and voice conversion, and then sends the results to the terminal.

[0272] A "voice profile" is a data set that describes the characteristics of a particular voice and is used for voice conversion.

[0273] "Preprocessing" refers to performing processes such as noise removal and volume normalization on recorded audio data to prepare it in a format that is easy for the generative AI model to handle.

[0274] "Generative AI model" refers to an artificial intelligence model used to convert input voice data into a different voice.

[0275] The "emotion engine" refers to a function that analyzes the user's emotional state from recorded voice data and automatically selects and corrects voice profiles based on the results.

[0276] A "communications protocol" refers to a technology that defines the procedures and rules for sending and receiving data over a network.

[0277] "Storage device" refers to hardware and software components for storing data.

[0278] "Encryption" refers to a conversion process that makes the contents of data unreadable to third parties.

[0279] "Noise reduction" refers to the process of removing unwanted background sounds from recorded audio data.

[0280] "Volume normalization" refers to the process of standardizing the volume level of recorded audio data and adjusting it to a certain standard.

[0281] MODE FOR CARRYING OUT THE INVENTION

[0282] This invention is a system that allows a user to record their voice, convert the voice on a server, and play back the converted voice. The system has the function of analyzing the user's emotional state and converting the voice based on the analysis results.

[0283] User operation

[0284] A user installs a dedicated application on a device such as a smartphone or PC. This application has the function of recording audio using the device's microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. The user then selects the voice profile they want to convert through the application's interface. This profile could include, for example, the voice of a specific anime character.

[0285] Data transmission and preprocessing

[0286] Once the user selects a profile, the device sends the recorded audio data and the selected audio profile information to the server. During this process, communication protocols such as HTTPS and TLS are used to ensure data security. Once the server receives the audio data, preprocessing begins. Software such as Adobe Audition or Audacity is used for preprocessing, which involves noise reduction and volume normalization.

[0287] Sentiment analysis and voice profile selection

[0288] Once preprocessing is complete, the voice data is passed to the emotion engine on the server. The emotion engine uses technologies such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state. Based on the analysis results, an appropriate voice profile is automatically selected or corrected. This function enables more natural voice conversion according to the user's emotions.

[0289] Audio data conversion

[0290] Next, based on the results of emotion analysis and voice profile selection, the generative AI model performs the conversion process. This process uses technologies from OpenAI GPT-3 and Google DeepMind, converting the input voice data into the voice of the specified profile. For example, if the user is recognized as excited, the voice is converted to an energetic voice to reflect that emotion.

[0291] Sending and playing converted audio

[0292] The converted voice data is temporarily stored on the server, then re-encrypted and sent to the user's device. When the device receives the converted voice data, a play button appears in the application. By pressing the play button, the converted voice is played back and the user can check the results.

[0293] Specific examples

[0294] For example, a user can record "hello" and input it into the application. If the emotion engine recognizes an excited state in the user's voice, it will convert it into an energetic voice based on the voice profile of the anime character selected by the user. The voice will then be sent to the user's device via the server, and the user will finally be able to hear "hello" in the excited voice of the anime character.

[0295] Prompt Sentence Examples

[0296] Take a recorded voice saying "Hello" and convert it into an energetic voice based on the voice profile of an excited anime character, then send it to the server.

[0297] In this way, the system of the present invention realizes voice conversion that reflects the user's emotional state, providing a more natural and appropriate means of communication.

[0298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0299] Step 1:

[0300] The user opens a dedicated application on a device such as a smartphone or PC. The user records audio by speaking into the microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. At this point, the input is the user's voice, and the output is the audio data stored on the device.

[0301] Step 2:

[0302] The user uses an interface within the application to select the voice profile they want to convert, for example, the voice of a particular anime character. Based on this selection, the device sends the recorded voice data and the selected voice profile information to the server. At this point, the input is the user's voice data and the selected voice profile, and the output is the voice data and profile information sent to the server.

[0303] Step 3:

[0304] The server performs preprocessing on the audio data it receives. Specifically, it uses software such as Adobe Audition or Audacity to perform noise reduction and volume normalization. Noise reduction detects and removes background noise, and volume normalization adjusts the audio volume to a certain standard. At this point, the input is the raw audio data sent to the server, and the output is the preprocessed audio data.

[0305] Step 4:

[0306] The preprocessed voice data is passed to the emotion engine on the server. The emotion engine uses tools such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state from the voice data. Based on the analysis results, the voice profile used by the generative AI model is automatically selected or corrected. At this point, the input is the preprocessed voice data, and the output is voice profile information based on the user's emotional state.

[0307] Step 5:

[0308] The generative AI model receives preprocessed voice data and the profile specified by the emotion engine as input, and converts the voice data into a different voice as specified. This process uses technologies from OpenAI GPT-3 and Google DeepMind. For example, if the user is recognized as excited, the voice is converted to an energetic one to reflect that emotion. At this point, the input is the preprocessed voice data and voice profile information, and the output is the converted voice data.

[0309] Step 6:

[0310] The server temporarily stores the converted voice data, re-encrypts it, and sends it to the user's device. The communication protocols used here include "HTTPS" and "TLS." The device receives the converted voice data and displays a play button in the application. When the user presses the play button, the converted voice is played back and the result can be confirmed. At this point, the input is the converted voice data, and the output is the voice played back on the user's device.

[0311] (Application example 2)

[0312] 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."

[0313] Conventional speech conversion systems convert speech without considering the user's emotional state, which means they are unable to convert speech appropriately based on the user's emotions. Furthermore, there is a lack of an interface for converting speech that reflects emotions, limiting the user experience. Furthermore, there is a lack of content distribution services that allow users to access speech conversion results in real time.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for recognizing the user's emotional state using an emotion engine and selecting or correcting a voice profile based on the recognition, means for transmitting the converted voice data to the user's terminal, and means for playing back the received converted voice data. This enables appropriate voice conversion that reflects the user's emotions, providing a rich user experience. Furthermore, real-time voice conversion according to the user's emotions can be realized in content distribution services.

[0315] "Means for recording user voice" refers to a function for acquiring the voice uttered by the user as digital voice data via a microphone.

[0316] The "means for transmitting recorded voice data to a server" is a function for uploading recorded voice data to a remote server via a network.

[0317] "Means for inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a function that performs preprocessing such as noise removal and volume normalization on speech data, and then converts it into a different speech using a machine learning model.

[0318] "Means for recognizing a user's emotional state using an emotion engine and selecting or correcting a voice profile based on that" refers to a function for analyzing voice data to identify a user's emotion and selecting or correcting an appropriate voice profile in accordance with that emotion.

[0319] The "means for transmitting converted voice data to the user's terminal" is a function for sending voice data converted by the generative model to the user's terminal via a network.

[0320] The "means for playing the received converted voice data" is a function for playing the converted voice data received by the user's terminal through a playback device.

[0321] The "pre-processing means for noise removal and volume normalization" is a function for removing environmental sounds and unnecessary sounds from recorded audio data and for equalizing variations in volume.

[0322] "Means for converting voice based on a voice profile selected by a generative model" is a function for converting a user's voice data into a target voice using a voice profile that has been pre-trained for voice conversion.

[0323] "Means for selecting from multiple types" is a function that provides an interface that allows users to select the audio profile they want to use from multiple options.

[0324] "Means for communication protocols and storage to ensure data security using encryption technology" refers to a function that applies encryption technology during communication and storage to maintain data security and prevent unauthorized access to voice data and other information.

[0325] System Overview

[0326] This invention is a service that records a user's voice and converts it into a different voice, and is a system that uses an emotion engine to recognize the user's emotions and reflect them in the converted voice. This system converts voices according to the user's emotions, allowing for richer communication and entertainment.

[0327] Components and Functions

[0328] 1. How to record your voice:

[0329] The microphone on a smartphone or computer is used to record the user's voice. Through a dedicated application, the user can record the voice and start recording by pressing the record button.

[0330] 2. How to send recorded audio data to the server:

[0331] Once the recording is complete, the user selects the voice profile they want to convert. After selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server. Data transmission is via the Internet.

[0332] 3. Preprocessing the audio data:

[0333] When the server receives the audio data, it first performs preprocessing such as noise reduction and volume normalization, and then inputs the preprocessed audio data into the generative AI model.

[0334] 4. Emotion Recognition with Emotion Engine:

[0335] The server is equipped with an emotion engine that analyzes the user's emotional state from recorded voice data. Based on this analysis, the generative model automatically selects or corrects the voice profile used, enabling voice conversion according to the user's emotions.

[0336] 5. Audio conversion process:

[0337] The generative AI model converts input voice data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic voice to reflect that emotion.

[0338] 6. Transmit and play converted audio:

[0339] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security. When the converted voice data is received by the device, a play button appears in the application, allowing the user to play back the converted voice and check the results.

[0340] Hardware and software used

[0341] Hardware:

[0342] Smartphone or computer microphone

[0343] software:

[0344] Dedicated application

[0345] A rigged emotional engine

[0346] Audio preprocessing and generative AI models

[0347] Encrypted communication protocols

[0348] Specific examples

[0349] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state from the user's voice. The user selects the voice of an animated character. In this case, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative AI model converts the voice based on that profile, and the converted voice is sent to the user's device via the server. Finally, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[0350] Prompt Sentence Examples

[0351] User's speech: "Hello"

[0352] Recognized emotion: Excitement

[0353] Selected voice profile: Energetic anime character

[0354] As described above, using this system makes it possible to convert voice according to the user's emotions, providing a richer communication experience that reflects emotions in real time.

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] The user records audio using a dedicated application. The user uses the microphone on their smartphone or computer to input audio by pressing the record button on the application. When recording is complete, the audio data is saved in temporary memory. The input is the user's audio data, and the output is the digital audio data saved in temporary memory.

[0358] Step 2:

[0359] The device sends the recorded voice data to the server. The user selects the voice profile they want to convert and presses the send button, which uploads the voice data and profile information to the server. The input is the recorded voice data and the selected voice profile, and the output is the data uploaded to the server.

[0360] Step 3:

[0361] The server performs preprocessing on the received audio data. Preprocessing includes noise removal and volume normalization, which converts the data into a form that is easy for the generative AI model to process. The input is the audio data uploaded to the server, and the output is preprocessed audio data that has been noise removed and volume normalized.

[0362] Step 4:

[0363] The server inputs the preprocessed speech data into an emotion engine to recognize the user's emotional state. The emotion engine analyzes the speech characteristics to identify the user's emotion (e.g., excitement, sadness, joy). The input is the preprocessed speech data, and the output is the recognized emotional state.

[0364] Step 5:

[0365] The server selects an appropriate voice profile for the generative AI model based on the output of the emotion engine (recognized emotional state). The profile is automatically applied depending on the emotion. The input is the emotional state and available voice profiles, and the output is the selected or corrected voice profile.

[0366] Step 6:

[0367] The server uses a generative AI model to convert the preprocessed voice data based on the selected voice profile. The generative AI model converts the voice into a new voice based on the training data. The input is the preprocessed voice data and the selected voice profile, and the output is the converted voice data.

[0368] Step 7:

[0369] The server sends the converted voice data to the user's device. Data security is ensured using encrypted communication. The input is the converted voice data, and the output is the voice data sent to the user's device.

[0370] Step 8:

[0371] The converted voice data received by the device is played back. A play button is displayed on the application, and the user can press it to play back the converted voice. The input is the voice data sent to the device, and the output is the voice played back on the application.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Second embodiment]

[0376] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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."

[0388] System Overview

[0389] The system is a service that allows users to record their own voice and convert it into a different voice, and consists of the following components:

[0390] A means of recording the user's voice

[0391] A means of sending recorded audio data to a server

[0392] A means to preprocess speech data on the server and input it into a generative model

[0393] A means of returning the audio converted by the generative model to the user

[0394] A means to play the converted audio

[0395] A means of recording the user's voice

[0396] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved on the device.

[0397] A means of sending recorded audio data to a server

[0398] Once the recording is complete, the user selects the voice profile they want to convert (e.g., the voice of the opposite sex or a specific voice actor). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[0399] A means to preprocess speech data on the server and input it into a generative model

[0400] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[0401] A means of converting audio using a generative model

[0402] A generative model converts input audio data into a specified voice based on a pre-trained voice profile, for example, converting a male voice into a female voice or changing the voice of a specific character.

[0403] A means of returning the converted audio to the user

[0404] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[0405] A means to play the converted audio

[0406] Once the user's device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[0407] Specific examples

[0408] For example, consider a case where a user records "Good morning" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "Good morning" is played in the anime character's voice.

[0409] By using this system, users can easily convert their own voice into a variety of voices, allowing them to experience new forms of enjoyment in communication and entertainment settings.

[0410] The processing flow will be explained below.

[0411] Step 1:

[0412] The user launches the application on their device and presses the record button to record their voice.

[0413] Step 2:

[0414] The device temporarily stores the recorded audio data in memory.

[0415] Step 3:

[0416] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[0417] Step 4:

[0418] The device packages the selected audio profile and the recorded audio data and sends it to the server.

[0419] Step 5:

[0420] The server receives the voice data package transmitted from the terminal.

[0421] Step 6:

[0422] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[0423] Step 7:

[0424] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[0425] Step 8:

[0426] The server performs a volume normalization process to adjust the volume level of the audio to a consistent standard.

[0427] Step 9:

[0428] The server prepares the preprocessed audio data to be input into the generative model.

[0429] Step 10:

[0430] A generative model analyzes the preprocessed audio data and transforms the audio based on a specified audio profile.

[0431] Step 11:

[0432] The generative model generates the converted audio data and returns it to the server.

[0433] Step 12:

[0434] The server stores the generated converted voice data in temporary storage.

[0435] Step 13:

[0436] The server then sends the converted audio data to the user's device using encryption technology.

[0437] Step 14:

[0438] The terminal receives the converted voice data from the server and stores it in temporary memory.

[0439] Step 15:

[0440] The user presses the application's play button to play the converted audio data.

[0441] Step 16:

[0442] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[0443] This processing flow allows users to easily convert their recorded voice into a different voice and see the results in real time.

[0444] Example 1

[0445] 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."

[0446] In today's world, there is a growing need for users to convert their own voice into that of another person or a specific character. However, conventional systems often lack the accuracy and processing speed of voice conversion, resulting in a poor user experience. Furthermore, data security is often not fully considered, creating privacy issues.

[0447] 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.

[0448] In this invention, the server includes a means for recording the user's voice, a means for transmitting the recorded voice data to the server, a means for preprocessing the voice data on the server and inputting the voice into a generative model to convert it into a different voice, a means for transmitting the converted voice data to the user's device, and a means for playing back the received converted voice data. This achieves high conversion accuracy and processing speed, allowing the user to experience fast and accurate voice conversion. In addition, the use of encryption technology ensures data security and protects the user's privacy.

[0449] "Means for recording the user's voice" refers to the means by which a user records voice through a microphone using a device such as a smartphone or computer.

[0450] The "means for transmitting recorded voice data to the server" refers to a means for transmitting voice data and related information from the user's terminal to the server.

[0451] "Means for preprocessing audio data on a server and inputting the audio into a generative model to convert it into a different audio" refers to a means for a server to receive audio data, perform preprocessing such as noise removal and volume normalization, and then use a generative AI model to convert the audio into a different specified audio.

[0452] The "means for transmitting converted voice data to the user's terminal" refers to a means for transmitting converted voice data from the server to the user's terminal.

[0453] The "means for reproducing the received converted voice data" refers to a means for reproducing the received voice data on the user's terminal.

[0454] "Denoising" is the process of removing unwanted background noise from audio data.

[0455] "Volume normalization" is the process of adjusting the volume level of audio data to a uniform level.

[0456] A "generative model" is an artificial intelligence model for converting speech into another speech based on a learned profile.

[0457] A "voice profile" is a set of pre-defined voice characteristic parameters that determine the characteristics of the converted voice.

[0458] "Encryption technology" is a technology for ensuring data security in communication protocols and storage.

[0459] System Overview

[0460] The system provides technology that allows users to record their own voice and convert it into a different voice. The system includes the following elements:

[0461] A means of recording the user's voice

[0462] A means of sending recorded audio data to a server

[0463] A means to preprocess speech data on the server and input it into a generative model

[0464] A means of converting speech using a generative model

[0465] A means of transmitting the converted audio data to the user's device

[0466] A means of playing the converted audio data

[0467] A means of recording the user's voice

[0468] Users use a dedicated application on their smartphone, PC, or other device to record their own voice using the microphone. Pressing the record button starts recording, and pressing the stop button saves the audio data on the device. For example, if a user says "Good morning," this voice will be recorded.

[0469] A means of sending recorded audio data to a server

[0470] Once the recording is complete, the user selects the voice profile they want to convert within the application, such as "female voice" or "anime character voice." The device then sends the recorded voice data and information about the selected voice profile to the server. The communication is encrypted using the HTTPS protocol.

[0471] A means to preprocess speech data on the server and input it into a generative model

[0472] When the server receives the audio data, it first performs preprocessing such as noise removal and volume normalization. This makes the audio data easier for the generative model to process. For example, it filters out background noise and adjusts volume peaks. The preprocessed audio data is then input into the generative model.

[0473] A means of converting audio using a generative model

[0474] The server inputs the preprocessed voice data into a generative AI model, which converts the voice based on the specified voice profile. For example, to convert a male voice into a female voice, it adjusts the frequency and timbre. The generative model uses a neural network, enabling highly accurate voice conversion.

[0475] A means of transmitting the converted audio data to the user's device

[0476] The converted audio data is temporarily stored on the server and then promptly sent to the user's device, also encrypted using the HTTPS protocol.

[0477] A means to play the converted audio

[0478] When the user's device receives the converted voice data, a play button will appear in the dedicated application. When the user presses the play button, the converted voice will be played and the user can check the results. For example, "Good morning" will be played in the voice of an anime character.

[0479] Examples of concrete examples and prompts

[0480] For example, consider a case where a user records "hello" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "hello" is played in the anime character's voice.

[0481] Examples of prompts used in this system include "Please convert this voice into a female voice" and "Please convert the recorded voice into the voice of an anime character."

[0482] With the above-described configuration and procedures, this system is able to convert a user's voice into another voice with high accuracy and provide it quickly and safely.

[0483] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0484] Step 1: Record the user's voice

[0485] The user launches a dedicated application and records their own voice using the microphone on their smartphone or computer. When the user taps the record button, voice recording begins, and the voice data up until the end of the recording is saved in the device's temporary memory. When the user taps the stop button, recording ends, and the voice data is saved in the device's storage. The user's voice is given as input, and the recorded voice data is saved as output. For example, the user might say, "Good morning."

[0486] Step 2: Send the recorded audio data to the server

[0487] Once the recording is complete, the user selects the voice profile they want to convert to within the application, for example, "female voice" or "anime character voice." After that, by tapping the send button, the device will send the recorded voice data and the selected voice profile information to the server, encrypting it using the HTTPS protocol. The recorded voice data and the selected voice profile are given as input, and sent to the server as output.

[0488] Step 3: Preprocess the audio data on the server

[0489] When the server receives the audio data, it performs preprocessing such as noise removal and volume normalization. Specifically, it filters out background noise and evens out volume peaks. Recorded audio data is given as input, and preprocessed audio data is generated as output. This converts the data into a format that is easy for the generative AI model to process.

[0490] Step 4: Input audio to the generative AI model and perform conversion

[0491] The preprocessed voice data is input into a generative AI model, which converts it into a different voice based on a specified voice profile. The generative AI model utilizes a trained neural network to convert a male voice into a female voice, or into the voice of a specific character. The preprocessed voice data and voice profile are given as input, and the converted voice data is generated as output. For example, "Good morning" is converted into a female voice.

[0492] Step 5: Send the converted audio data to the user's device

[0493] The converted voice data is temporarily stored on the server and then encrypted using the HTTPS protocol before being sent to the user's device. The converted voice data is given as input and sent to the user's device as output.

[0494] Step 6: Play the converted audio

[0495] When the user's device receives the converted voice data, a play button is displayed within the dedicated application. When the user presses the play button, the converted voice is played. The converted voice data is given as input, and the voice is played as output. For example, when the user presses the play button, "Good morning" is played in the voice of an anime character.

[0496] Through the above processing steps, users can convert their own voice with high accuracy based on various profiles and enjoy it quickly and safely.

[0497] (Application example 1)

[0498] 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."

[0499] Industrial facilities require guidance in multiple languages ​​or with specific character voices, but current systems only support one language and have limited guidance methods. Furthermore, users have limited means of understanding the progress of the process of converting recorded voices into different voices, making efficient communication difficult. Furthermore, robots that provide guidance in industrial facilities require advanced technology to convert voices in real time, but current technology makes this difficult.

[0500] 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.

[0501] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for transmitting the converted voice data to the user's terminal, means for playing back the received converted voice data, means for converting the voice in the robot using a specific voice profile to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and means for displaying the processing status until the recorded voice data is converted. This makes it possible to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and allows users (e.g., visitors or workers) to understand the progress of the voice conversion in real time, thereby achieving efficient communication.

[0502] "Means for recording the user's voice" means means including a microphone or recording device that a user uses to record their voice.

[0503] The "means for transmitting recorded voice data to a server" refers to a means for sending recorded voice data to a server via a network.

[0504] "The effort of inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a method of inputting speech data that has been preprocessed, such as by noise removal and volume normalization, into a generative model and converting it into a specified different speech.

[0505] The "means for transmitting converted voice data to a user's terminal" refers to a means for transmitting voice data converted by a generative model to a user's terminal via a network.

[0506] The "means for reproducing the received converted voice data" refers to a means for reproducing the converted voice data received at the user's terminal.

[0507] "Means for converting voice using a specific voice profile and providing guidance or instructions within an industrial facility in multiple languages ​​or in the voice of a specific character" refers to means for converting voice using a specific voice profile and providing information in different languages ​​or in the voice of a specific character when providing guidance or instructions within an industrial facility.

[0508] The "means for displaying the processing status until the recorded audio data is converted" is a means for displaying to the user the progress status until the conversion process is completed.

[0509] The system for implementing this invention records the user's voice, converts it into a different voice, and provides guidance and instructions within an industrial facility. The overall configuration and operation of the system will be described in detail below.

[0510] System Overview

[0511] The system consists of the following components:

[0512] How to record user voice: The user can record their own voice using the microphone installed on the robot. Recording starts when the start button is pressed, and ends when the stop button is pressed.

[0513] A method for transmitting recorded audio data to a server: Once recording is complete, the audio data is transmitted via wireless communication to a server, which receives the data securely using Internet protocols (e.g., HTTPS).

[0514] A means of inputting preprocessed audio data into a generative model: The server performs preprocessing (noise removal and volume normalization) on the received audio data. The preprocessed data is input into a generative AI model, where it is converted based on a specified audio profile. An example of this generative AI model is Hugging Face's Wav2Vec2.

[0515] A method for sending the converted voice data to the user's terminal: The converted voice data is sent from the server to the robot, which receives the voice data, checks it, and plays it back.

[0516] A means for playing the received converted voice data: The robot is equipped with a speaker and plays the converted voice, allowing it to provide guidance to users and visitors in multiple languages ​​or with the voices of different characters.

[0517] A means of displaying the processing status while the recorded audio data is being converted: The user can check the progress of the recording and conversion on the robot's display or in the mobile application.

[0518] Examples of technologies used

[0519] Hardware: A robot with a microphone for recording audio, a speaker for playing audio, and a network interface to support wireless communication.

[0520] Software: The application that controls the audio recording and playback uses the sounddevice library. For audio conversion, we use a generative AI model (e.g., Hugging Face's Wav2Vec2). The entire data processing and communication uses the secure HTTP communication protocol (HTTPS).

[0521] Specific examples

[0522] For example, consider a scenario where a guide robot in a factory needs to guide visitors, saying "Hello, please come this way." The user records this message through a microphone and selects a specific voice profile (e.g., a female Japanese voice). Once the recording is complete, the audio data is sent to a server where it is preprocessed and converted by a generative AI model. The converted audio data is then sent back to the robot and played back to the visitor.

[0523] Example prompt sentence:

[0524] "In: Hello, please come in

[0525] Out: Japanese female voice

[0526] These methods allow guidance and instructions within industrial facilities to be given in multiple languages ​​or in the voice of a specific character, thereby achieving efficient communication.

[0527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0528] Step 1:

[0529] Recording user voice

[0530] When the user presses the recording start button on the robot, the robot's microphone records the voice. The user's voice signal is picked up through the microphone and stored in the device as digital voice data. At this time, the input is the user's voice, and the output is digitized voice data.

[0531] Step 2:

[0532] Sending recording data to the server

[0533] When the user stops recording, the recorded audio data is sent to the server via wireless communication. The HTTPS protocol is used for transmission, ensuring data security. The input here is the recorded audio data, and the output is the data sent to the server.

[0534] Step 3:

[0535] Preprocessing by the server

[0536] The server performs preprocessing on the received audio data. Specifically, it removes noise and normalizes the volume to make the data easier for the generative model to process. The input is the transmitted audio data, and the output is the preprocessed audio data.

[0537] Step 4:

[0538] Voice conversion using generative AI models

[0539] The preprocessed voice data is input to a generative AI model on the server (e.g., Hugging Face's Wav2Vec2), and the voice is converted based on a specified voice profile (e.g., Japanese female voice). An example of a prompt sentence is "In: Hello, please come this way, Out: Japanese female voice." The input is the preprocessed voice data and the voice profile, and the output is the converted voice data.

[0540] Step 5:

[0541] Sending and receiving converted data

[0542] The converted voice data is sent from the server to the robot. The robot's terminal stores the received data and prepares it for playback. The input is the converted voice data, and the output is the data sent to the robot.

[0543] Step 6:

[0544] Playing audio data

[0545] The robot receives the converted data and plays it back through the speaker, allowing users and visitors to listen to the converted audio in real time. The input is the received converted audio data, and the output is the audio that is played back.

[0546] Step 7:

[0547] Viewing Processing Status

[0548] During the conversion process of the voice data, the processing status is displayed on the robot's display and in the mobile application. The user can check the progress. The input is the processing status information, and the output is the status display.

[0549] 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.

[0550] System Overview

[0551] The system is a service that records a user's voice and converts it into a different voice, and also incorporates an emotion engine that recognizes the user's emotions and reflects them in the converted voice. It consists of the following components:

[0552] A means of recording the user's voice

[0553] A means of sending recorded audio data to a server

[0554] Preprocessing method for noise reduction and volume normalization of audio data

[0555] A means of inputting preprocessed speech data into a generative model to convert it into a different speech.

[0556] A means of using an emotion engine to recognize a user's emotional state and select or correct a voice profile based on that

[0557] A means of transmitting the converted audio data to the user's device

[0558] A means for playing the received converted audio data

[0559] A means of recording the user's voice

[0560] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved in the device's temporary memory.

[0561] A means of sending recorded audio data to a server

[0562] Once the recording is complete, the user selects the voice profile they want to convert (for example, the voice of a specific character). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[0563] Audio data preprocessing

[0564] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[0565] Emotion recognition by emotion engine

[0566] The server is equipped with an emotion engine that analyzes the user's emotional state from the recorded voice data. Based on this analysis, the voice profile used by the generative model is automatically selected or corrected, enabling more appropriate voice conversion according to the user's emotions.

[0567] Audio conversion processing

[0568] The generative model converts input speech data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic one to reflect that emotion.

[0569] Transmit and play converted audio

[0570] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[0571] When the device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[0572] Specific examples

[0573] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state based on the user's voice. If the user selects the voice of an animated character, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative model then converts the voice based on that profile, and the converted voice is sent to the user's device via the server. As a result, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[0574] By using this system, users can easily convert their own voice into a variety of voices and even convert the voice according to emotions, providing a richer communication and entertainment experience.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] The user launches the application on their device and presses the record button to record their voice.

[0578] Step 2:

[0579] The device temporarily stores the recorded audio data in memory.

[0580] Step 3:

[0581] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[0582] Step 4:

[0583] The device sends the recorded audio data and information about the selected audio profile to the server.

[0584] Step 5:

[0585] The server receives the voice data package transmitted from the terminal.

[0586] Step 6:

[0587] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[0588] Step 7:

[0589] The server's emotion engine analyzes the recorded voice data and recognizes the user's emotional state.

[0590] Step 8:

[0591] The server automatically adjusts or selects the voice profile used by the generative model based on the user's emotional state.

[0592] Step 9:

[0593] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[0594] Step 10:

[0595] The server performs volume normalization, adjusting the volume level of the recorded audio data to a consistent standard level.

[0596] Step 11:

[0597] The server prepares the preprocessed audio data to be input into the generative model.

[0598] Step 12:

[0599] The generative model analyzes the preprocessed speech data and converts it into a specified alternative voice based on information provided by the emotion engine.

[0600] Step 13:

[0601] The generative model generates the converted audio data and returns it to the server.

[0602] Step 14:

[0603] The server stores the generated converted voice data in temporary storage.

[0604] Step 15:

[0605] The server then sends the converted audio data to the user's device using encryption technology.

[0606] Step 16:

[0607] The terminal receives the converted voice data from the server and stores it in temporary memory.

[0608] Step 17:

[0609] The user presses the application's play button to play the converted audio data.

[0610] Step 18:

[0611] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[0612] This process allows users to convert their recorded voice into different voices depending on their emotions and see the results in real time. For example, if the user is feeling "happy," the converted voice will reflect that emotion and be created with a happier tone.

[0613] Example 2

[0614] 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."

[0615] Conventional voice conversion systems have difficulty in converting voices that reflect the user's emotions, making it difficult to generate appropriate voices according to emotions. Ensuring the security of voice data and the stability of the voice conversion process are also issues. To solve these problems, a system with the ability to analyze the user's emotions and automatically select or correct a voice profile according to those emotions is needed.

[0616] 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.

[0617] In this invention, the server includes a means for analyzing the user's emotional state and automatically selecting or correcting a voice profile based on the analysis, a preprocessing means for performing noise reduction and volume normalization on recorded voice data, and a means for requesting a generative AI model to convert input voice data into a different voice based on a pre-trained voice profile. This enables voice conversion according to the user's emotions, resulting in more natural and appropriate communication. Data security is also ensured by the communication protocol and data encryption in the storage device.

[0618] "User" refers to the person who uses the system to record audio and play back the converted audio.

[0619] "Terminal" refers to an electronic device (such as a smartphone or PC) that is owned by the user and that records audio and communicates with the server.

[0620] "Server" refers to a computer system that performs preprocessing of voice data sent from a terminal, emotion analysis, voice profile selection, and voice conversion, and then sends the results to the terminal.

[0621] A "voice profile" is a data set that describes the characteristics of a particular voice and is used for voice conversion.

[0622] "Preprocessing" refers to performing processes such as noise removal and volume normalization on recorded audio data to prepare it in a format that is easy for the generative AI model to handle.

[0623] "Generative AI model" refers to an artificial intelligence model used to convert input voice data into a different voice.

[0624] The "emotion engine" refers to a function that analyzes the user's emotional state from recorded voice data and automatically selects and corrects voice profiles based on the results.

[0625] A "communications protocol" refers to a technology that defines the procedures and rules for sending and receiving data over a network.

[0626] "Storage device" refers to hardware and software components for storing data.

[0627] "Encryption" refers to a conversion process that makes the contents of data unreadable to third parties.

[0628] "Noise reduction" refers to the process of removing unwanted background sounds from recorded audio data.

[0629] "Volume normalization" refers to the process of standardizing the volume level of recorded audio data and adjusting it to a certain standard.

[0630] MODE FOR CARRYING OUT THE INVENTION

[0631] This invention is a system that allows a user to record their voice, convert the voice on a server, and play back the converted voice. The system has the function of analyzing the user's emotional state and converting the voice based on the analysis results.

[0632] User operation

[0633] A user installs a dedicated application on a device such as a smartphone or PC. This application has the function of recording audio using the device's microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. The user then selects the voice profile they want to convert through the application's interface. This profile could include, for example, the voice of a specific anime character.

[0634] Data transmission and preprocessing

[0635] Once the user selects a profile, the device sends the recorded audio data and the selected audio profile information to the server. During this process, communication protocols such as HTTPS and TLS are used to ensure data security. Once the server receives the audio data, preprocessing begins. Software such as Adobe Audition or Audacity is used for preprocessing, which involves noise reduction and volume normalization.

[0636] Sentiment analysis and voice profile selection

[0637] Once preprocessing is complete, the voice data is passed to the emotion engine on the server. The emotion engine uses technologies such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state. Based on the analysis results, an appropriate voice profile is automatically selected or corrected. This function enables more natural voice conversion according to the user's emotions.

[0638] Audio data conversion

[0639] Next, based on the results of emotion analysis and voice profile selection, the generative AI model performs the conversion process. This process uses technologies from OpenAI GPT-3 and Google DeepMind, converting the input voice data into the voice of the specified profile. For example, if the user is recognized as excited, the voice is converted to an energetic voice to reflect that emotion.

[0640] Sending and playing converted audio

[0641] The converted voice data is temporarily stored on the server, then re-encrypted and sent to the user's device. When the device receives the converted voice data, a play button appears in the application. By pressing the play button, the converted voice is played back and the user can check the results.

[0642] Specific examples

[0643] For example, a user can record "hello" and input it into the application. If the emotion engine recognizes an excited state in the user's voice, it will convert it into an energetic voice based on the voice profile of the anime character selected by the user. The voice will then be sent to the user's device via the server, and the user will finally be able to hear "hello" in the excited voice of the anime character.

[0644] Prompt Sentence Examples

[0645] Take a recorded voice saying "Hello" and convert it into an energetic voice based on the voice profile of an excited anime character, then send it to the server.

[0646] In this way, the system of the present invention realizes voice conversion that reflects the user's emotional state, providing a more natural and appropriate means of communication.

[0647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0648] Step 1:

[0649] The user opens a dedicated application on a device such as a smartphone or PC. The user records audio by speaking into the microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. At this point, the input is the user's voice, and the output is the audio data stored on the device.

[0650] Step 2:

[0651] The user uses an interface within the application to select the voice profile they want to convert, for example, the voice of a particular anime character. Based on this selection, the device sends the recorded voice data and the selected voice profile information to the server. At this point, the input is the user's voice data and the selected voice profile, and the output is the voice data and profile information sent to the server.

[0652] Step 3:

[0653] The server performs preprocessing on the audio data it receives. Specifically, it uses software such as Adobe Audition or Audacity to perform noise reduction and volume normalization. Noise reduction detects and removes background noise, and volume normalization adjusts the audio volume to a certain standard. At this point, the input is the raw audio data sent to the server, and the output is the preprocessed audio data.

[0654] Step 4:

[0655] The preprocessed voice data is passed to the emotion engine on the server. The emotion engine uses tools such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state from the voice data. Based on the analysis results, the voice profile used by the generative AI model is automatically selected or corrected. At this point, the input is the preprocessed voice data, and the output is voice profile information based on the user's emotional state.

[0656] Step 5:

[0657] The generative AI model receives preprocessed voice data and the profile specified by the emotion engine as input, and converts the voice data into a different voice as specified. This process uses technologies from OpenAI GPT-3 and Google DeepMind. For example, if the user is recognized as excited, the voice is converted to an energetic one to reflect that emotion. At this point, the input is the preprocessed voice data and voice profile information, and the output is the converted voice data.

[0658] Step 6:

[0659] The server temporarily stores the converted voice data, re-encrypts it, and sends it to the user's device. The communication protocols used here include "HTTPS" and "TLS." The device receives the converted voice data and displays a play button in the application. When the user presses the play button, the converted voice is played back and the result can be confirmed. At this point, the input is the converted voice data, and the output is the voice played back on the user's device.

[0660] (Application example 2)

[0661] 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."

[0662] Conventional speech conversion systems convert speech without considering the user's emotional state, which means they are unable to convert speech appropriately based on the user's emotions. Furthermore, there is a lack of an interface for converting speech that reflects emotions, limiting the user experience. Furthermore, there is a lack of content distribution services that allow users to access speech conversion results in real time.

[0663] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for recognizing the user's emotional state using an emotion engine and selecting or correcting a voice profile based on the recognition, means for transmitting the converted voice data to the user's terminal, and means for playing back the received converted voice data. This enables appropriate voice conversion that reflects the user's emotions, providing a rich user experience. Furthermore, real-time voice conversion according to the user's emotions can be realized in content distribution services.

[0664] "Means for recording user voice" refers to a function for acquiring the voice uttered by the user as digital voice data via a microphone.

[0665] The "means for transmitting recorded voice data to a server" is a function for uploading recorded voice data to a remote server via a network.

[0666] "Means for inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a function that performs preprocessing such as noise removal and volume normalization on speech data, and then converts it into a different speech using a machine learning model.

[0667] "Means for recognizing a user's emotional state using an emotion engine and selecting or correcting a voice profile based on that" refers to a function for analyzing voice data to identify a user's emotion and selecting or correcting an appropriate voice profile in accordance with that emotion.

[0668] The "means for transmitting converted voice data to the user's terminal" is a function for sending voice data converted by the generative model to the user's terminal via a network.

[0669] The "means for playing the received converted voice data" is a function for playing the converted voice data received by the user's terminal through a playback device.

[0670] The "pre-processing means for noise removal and volume normalization" is a function for removing environmental sounds and unnecessary sounds from recorded audio data and for equalizing variations in volume.

[0671] "Means for converting voice based on a voice profile selected by a generative model" is a function for converting a user's voice data into a target voice using a voice profile that has been pre-trained for voice conversion.

[0672] "Means for selecting from multiple types" is a function that provides an interface that allows users to select the audio profile they want to use from multiple options.

[0673] "Means for communication protocols and storage to ensure data security using encryption technology" refers to a function that applies encryption technology during communication and storage to maintain data security and prevent unauthorized access to voice data and other information.

[0674] System Overview

[0675] This invention is a service that records a user's voice and converts it into a different voice, and is a system that uses an emotion engine to recognize the user's emotions and reflect them in the converted voice. This system converts voices according to the user's emotions, allowing for richer communication and entertainment.

[0676] Components and Functions

[0677] 1. How to record your voice:

[0678] The microphone on a smartphone or computer is used to record the user's voice. Through a dedicated application, the user can record the voice and start recording by pressing the record button.

[0679] 2. How to send recorded audio data to the server:

[0680] Once the recording is complete, the user selects the voice profile they want to convert. After selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server. Data transmission is via the Internet.

[0681] 3. Preprocessing the audio data:

[0682] When the server receives the audio data, it first performs preprocessing such as noise reduction and volume normalization, and then inputs the preprocessed audio data into the generative AI model.

[0683] 4. Emotion Recognition with Emotion Engine:

[0684] The server is equipped with an emotion engine that analyzes the user's emotional state from recorded voice data. Based on this analysis, the generative model automatically selects or corrects the voice profile used, enabling voice conversion according to the user's emotions.

[0685] 5. Audio conversion process:

[0686] The generative AI model converts input voice data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic voice to reflect that emotion.

[0687] 6. Transmit and play converted audio:

[0688] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security. When the converted voice data is received by the device, a play button appears in the application, allowing the user to play back the converted voice and check the results.

[0689] Hardware and software used

[0690] Hardware:

[0691] Smartphone or computer microphone

[0692] software:

[0693] Dedicated application

[0694] A rigged emotional engine

[0695] Audio preprocessing and generative AI models

[0696] Encrypted communication protocols

[0697] Specific examples

[0698] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state from the user's voice. The user selects the voice of an animated character. In this case, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative AI model converts the voice based on that profile, and the converted voice is sent to the user's device via the server. Finally, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[0699] Prompt Sentence Examples

[0700] User's speech: "Hello"

[0701] Recognized emotion: Excitement

[0702] Selected voice profile: Energetic anime character

[0703] As described above, using this system makes it possible to convert voice according to the user's emotions, providing a richer communication experience that reflects emotions in real time.

[0704] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0705] Step 1:

[0706] The user records audio using a dedicated application. The user uses the microphone on their smartphone or computer to input audio by pressing the record button on the application. When recording is complete, the audio data is saved in temporary memory. The input is the user's audio data, and the output is the digital audio data saved in temporary memory.

[0707] Step 2:

[0708] The device sends the recorded voice data to the server. The user selects the voice profile they want to convert and presses the send button, which uploads the voice data and profile information to the server. The input is the recorded voice data and the selected voice profile, and the output is the data uploaded to the server.

[0709] Step 3:

[0710] The server performs preprocessing on the received audio data. Preprocessing includes noise removal and volume normalization, which converts the data into a form that is easy for the generative AI model to process. The input is the audio data uploaded to the server, and the output is preprocessed audio data that has been noise removed and volume normalized.

[0711] Step 4:

[0712] The server inputs the preprocessed speech data into an emotion engine to recognize the user's emotional state. The emotion engine analyzes the speech characteristics to identify the user's emotion (e.g., excitement, sadness, joy). The input is the preprocessed speech data, and the output is the recognized emotional state.

[0713] Step 5:

[0714] The server selects an appropriate voice profile for the generative AI model based on the output of the emotion engine (recognized emotional state). The profile is automatically applied depending on the emotion. The input is the emotional state and available voice profiles, and the output is the selected or corrected voice profile.

[0715] Step 6:

[0716] The server uses a generative AI model to convert the preprocessed voice data based on the selected voice profile. The generative AI model converts the voice into a new voice based on the training data. The input is the preprocessed voice data and the selected voice profile, and the output is the converted voice data.

[0717] Step 7:

[0718] The server sends the converted voice data to the user's device. Data security is ensured using encrypted communication. The input is the converted voice data, and the output is the voice data sent to the user's device.

[0719] Step 8:

[0720] The converted voice data received by the device is played back. A play button is displayed on the application, and the user can press it to play back the converted voice. The input is the voice data sent to the device, and the output is the voice played back on the application.

[0721] 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.

[0722] 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.

[0723] 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.

[0724] [Third embodiment]

[0725] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0726] 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.

[0727] 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).

[0728] 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.

[0729] 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.

[0730] 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).

[0731] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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."

[0737] System Overview

[0738] The system is a service that allows users to record their own voice and convert it into a different voice, and consists of the following components:

[0739] A means of recording the user's voice

[0740] A means of sending recorded audio data to a server

[0741] A means to preprocess speech data on the server and input it into a generative model

[0742] A means of returning the audio converted by the generative model to the user

[0743] A means to play the converted audio

[0744] A means of recording the user's voice

[0745] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved on the device.

[0746] A means of sending recorded audio data to a server

[0747] Once the recording is complete, the user selects the voice profile they want to convert (e.g., the voice of the opposite sex or a specific voice actor). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[0748] A means to preprocess speech data on the server and input it into a generative model

[0749] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[0750] A means of converting audio using a generative model

[0751] A generative model converts input audio data into a specified voice based on a pre-trained voice profile, for example, converting a male voice into a female voice or changing the voice of a specific character.

[0752] A means of returning the converted audio to the user

[0753] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[0754] A means to play the converted audio

[0755] Once the user's device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[0756] Specific examples

[0757] For example, consider a case where a user records "Good morning" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "Good morning" is played in the anime character's voice.

[0758] By using this system, users can easily convert their own voice into a variety of voices, allowing them to experience new forms of enjoyment in communication and entertainment settings.

[0759] The processing flow will be explained below.

[0760] Step 1:

[0761] The user launches the application on their device and presses the record button to record their voice.

[0762] Step 2:

[0763] The device temporarily stores the recorded audio data in memory.

[0764] Step 3:

[0765] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[0766] Step 4:

[0767] The device packages the selected audio profile and the recorded audio data and sends it to the server.

[0768] Step 5:

[0769] The server receives the voice data package transmitted from the terminal.

[0770] Step 6:

[0771] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[0772] Step 7:

[0773] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[0774] Step 8:

[0775] The server performs a volume normalization process to adjust the volume level of the audio to a consistent standard.

[0776] Step 9:

[0777] The server prepares the preprocessed audio data to be input into the generative model.

[0778] Step 10:

[0779] A generative model analyzes the preprocessed audio data and transforms the audio based on a specified audio profile.

[0780] Step 11:

[0781] The generative model generates the converted audio data and returns it to the server.

[0782] Step 12:

[0783] The server stores the generated converted voice data in temporary storage.

[0784] Step 13:

[0785] The server then sends the converted audio data to the user's device using encryption technology.

[0786] Step 14:

[0787] The terminal receives the converted voice data from the server and stores it in temporary memory.

[0788] Step 15:

[0789] The user presses the application's play button to play the converted audio data.

[0790] Step 16:

[0791] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[0792] This processing flow allows users to easily convert their recorded voice into a different voice and see the results in real time.

[0793] Example 1

[0794] 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."

[0795] In today's world, there is a growing need for users to convert their own voice into that of another person or a specific character. However, conventional systems often lack the accuracy and processing speed of voice conversion, resulting in a poor user experience. Furthermore, data security is often not fully considered, creating privacy issues.

[0796] 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.

[0797] In this invention, the server includes a means for recording the user's voice, a means for transmitting the recorded voice data to the server, a means for preprocessing the voice data on the server and inputting the voice into a generative model to convert it into a different voice, a means for transmitting the converted voice data to the user's device, and a means for playing back the received converted voice data. This achieves high conversion accuracy and processing speed, allowing the user to experience fast and accurate voice conversion. In addition, the use of encryption technology ensures data security and protects the user's privacy.

[0798] "Means for recording the user's voice" refers to the means by which a user records voice through a microphone using a device such as a smartphone or computer.

[0799] The "means for transmitting recorded voice data to the server" refers to a means for transmitting voice data and related information from the user's terminal to the server.

[0800] "Means for preprocessing audio data on a server and inputting the audio into a generative model to convert it into a different audio" refers to a means for a server to receive audio data, perform preprocessing such as noise removal and volume normalization, and then use a generative AI model to convert the audio into a different specified audio.

[0801] The "means for transmitting converted voice data to the user's terminal" refers to a means for transmitting converted voice data from the server to the user's terminal.

[0802] The "means for reproducing the received converted voice data" refers to a means for reproducing the received voice data on the user's terminal.

[0803] "Denoising" is the process of removing unwanted background noise from audio data.

[0804] "Volume normalization" is the process of adjusting the volume level of audio data to a uniform level.

[0805] A "generative model" is an artificial intelligence model for converting speech into another speech based on a learned profile.

[0806] A "voice profile" is a set of pre-defined voice characteristic parameters that determine the characteristics of the converted voice.

[0807] "Encryption technology" is a technology for ensuring data security in communication protocols and storage.

[0808] System Overview

[0809] The system provides technology that allows users to record their own voice and convert it into a different voice. The system includes the following elements:

[0810] A means of recording the user's voice

[0811] A means of sending recorded audio data to a server

[0812] A means to preprocess speech data on the server and input it into a generative model

[0813] A means of converting speech using a generative model

[0814] A means of transmitting the converted audio data to the user's device

[0815] A means of playing the converted audio data

[0816] A means of recording the user's voice

[0817] Users use a dedicated application on their smartphone, PC, or other device to record their own voice using the microphone. Pressing the record button starts recording, and pressing the stop button saves the audio data on the device. For example, if a user says "Good morning," this voice will be recorded.

[0818] A means of sending recorded audio data to a server

[0819] Once the recording is complete, the user selects the voice profile they want to convert within the application, such as "female voice" or "anime character voice." The device then sends the recorded voice data and information about the selected voice profile to the server. The communication is encrypted using the HTTPS protocol.

[0820] A means to preprocess speech data on the server and input it into a generative model

[0821] When the server receives the audio data, it first performs preprocessing such as noise removal and volume normalization. This makes the audio data easier for the generative model to process. For example, it filters out background noise and adjusts volume peaks. The preprocessed audio data is then input into the generative model.

[0822] A means of converting audio using a generative model

[0823] The server inputs the preprocessed voice data into a generative AI model, which converts the voice based on the specified voice profile. For example, to convert a male voice into a female voice, it adjusts the frequency and timbre. The generative model uses a neural network, enabling highly accurate voice conversion.

[0824] A means of transmitting the converted audio data to the user's device

[0825] The converted audio data is temporarily stored on the server and then promptly sent to the user's device, also encrypted using the HTTPS protocol.

[0826] A means to play the converted audio

[0827] When the user's device receives the converted voice data, a play button will appear in the dedicated application. When the user presses the play button, the converted voice will be played and the user can check the results. For example, "Good morning" will be played in the voice of an anime character.

[0828] Examples of concrete examples and prompts

[0829] For example, consider a case where a user records "hello" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "hello" is played in the anime character's voice.

[0830] Examples of prompts used in this system include "Please convert this voice into a female voice" and "Please convert the recorded voice into the voice of an anime character."

[0831] With the above-described configuration and procedures, this system is able to convert a user's voice into another voice with high accuracy and provide it quickly and safely.

[0832] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0833] Step 1: Record the user's voice

[0834] The user launches a dedicated application and records their own voice using the microphone on their smartphone or computer. When the user taps the record button, voice recording begins, and the voice data up until the end of the recording is saved in the device's temporary memory. When the user taps the stop button, recording ends, and the voice data is saved in the device's storage. The user's voice is given as input, and the recorded voice data is saved as output. For example, the user might say, "Good morning."

[0835] Step 2: Send the recorded audio data to the server

[0836] Once the recording is complete, the user selects the voice profile they want to convert to within the application, for example, "female voice" or "anime character voice." After that, by tapping the send button, the device will send the recorded voice data and the selected voice profile information to the server, encrypting it using the HTTPS protocol. The recorded voice data and the selected voice profile are given as input, and sent to the server as output.

[0837] Step 3: Preprocess the audio data on the server

[0838] When the server receives the audio data, it performs preprocessing such as noise removal and volume normalization. Specifically, it filters out background noise and evens out volume peaks. Recorded audio data is given as input, and preprocessed audio data is generated as output. This converts the data into a format that is easy for the generative AI model to process.

[0839] Step 4: Input audio to the generative AI model and perform conversion

[0840] The preprocessed voice data is input into a generative AI model, which converts it into a different voice based on a specified voice profile. The generative AI model utilizes a trained neural network to convert a male voice into a female voice, or into the voice of a specific character. The preprocessed voice data and voice profile are given as input, and the converted voice data is generated as output. For example, "Good morning" is converted into a female voice.

[0841] Step 5: Send the converted audio data to the user's device

[0842] The converted voice data is temporarily stored on the server and then encrypted using the HTTPS protocol before being sent to the user's device. The converted voice data is given as input and sent to the user's device as output.

[0843] Step 6: Play the converted audio

[0844] When the user's device receives the converted voice data, a play button is displayed within the dedicated application. When the user presses the play button, the converted voice is played. The converted voice data is given as input, and the voice is played as output. For example, when the user presses the play button, "Good morning" is played in the voice of an anime character.

[0845] Through the above processing steps, users can convert their own voice with high accuracy based on various profiles and enjoy it quickly and safely.

[0846] (Application example 1)

[0847] 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."

[0848] Industrial facilities require guidance in multiple languages ​​or with specific character voices, but current systems only support one language and have limited guidance methods. Furthermore, users have limited means of understanding the progress of the process of converting recorded voices into different voices, making efficient communication difficult. Furthermore, robots that provide guidance in industrial facilities require advanced technology to convert voices in real time, but current technology makes this difficult.

[0849] 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.

[0850] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for transmitting the converted voice data to the user's terminal, means for playing back the received converted voice data, means for converting the voice in the robot using a specific voice profile to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and means for displaying the processing status until the recorded voice data is converted. This makes it possible to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and allows users (e.g., visitors or workers) to understand the progress of the voice conversion in real time, thereby achieving efficient communication.

[0851] "Means for recording the user's voice" means means including a microphone or recording device that a user uses to record their voice.

[0852] The "means for transmitting recorded voice data to a server" refers to a means for sending recorded voice data to a server via a network.

[0853] "The effort of inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a method of inputting speech data that has been preprocessed, such as by noise removal and volume normalization, into a generative model and converting it into a specified different speech.

[0854] The "means for transmitting converted voice data to a user's terminal" refers to a means for transmitting voice data converted by a generative model to a user's terminal via a network.

[0855] The "means for reproducing the received converted voice data" refers to a means for reproducing the converted voice data received at the user's terminal.

[0856] "Means for converting voice using a specific voice profile and providing guidance or instructions within an industrial facility in multiple languages ​​or in the voice of a specific character" refers to means for converting voice using a specific voice profile and providing information in different languages ​​or in the voice of a specific character when providing guidance or instructions within an industrial facility.

[0857] The "means for displaying the processing status until the recorded audio data is converted" is a means for displaying to the user the progress status until the conversion process is completed.

[0858] The system for implementing this invention records the user's voice, converts it into a different voice, and provides guidance and instructions within an industrial facility. The overall configuration and operation of the system will be described in detail below.

[0859] System Overview

[0860] The system consists of the following components:

[0861] How to record user voice: The user can record their own voice using the microphone installed on the robot. Recording starts when the start button is pressed, and ends when the stop button is pressed.

[0862] A method for transmitting recorded audio data to a server: Once recording is complete, the audio data is transmitted via wireless communication to a server, which receives the data securely using Internet protocols (e.g., HTTPS).

[0863] A means of inputting preprocessed audio data into a generative model: The server performs preprocessing (noise removal and volume normalization) on the received audio data. The preprocessed data is input into a generative AI model, where it is converted based on a specified audio profile. An example of this generative AI model is Hugging Face's Wav2Vec2.

[0864] A method for sending the converted voice data to the user's terminal: The converted voice data is sent from the server to the robot, which receives the voice data, checks it, and plays it back.

[0865] A means for playing the received converted voice data: The robot is equipped with a speaker and plays the converted voice, allowing it to provide guidance to users and visitors in multiple languages ​​or with the voices of different characters.

[0866] A means of displaying the processing status while the recorded audio data is being converted: The user can check the progress of the recording and conversion on the robot's display or in the mobile application.

[0867] Examples of technologies used

[0868] Hardware: A robot with a microphone for recording audio, a speaker for playing audio, and a network interface to support wireless communication.

[0869] Software: The application that controls the audio recording and playback uses the sounddevice library. For audio conversion, we use a generative AI model (e.g., Hugging Face's Wav2Vec2). The entire data processing and communication uses the secure HTTP communication protocol (HTTPS).

[0870] Specific examples

[0871] For example, consider a scenario where a guide robot in a factory needs to guide visitors, saying "Hello, please come this way." The user records this message through a microphone and selects a specific voice profile (e.g., a female Japanese voice). Once the recording is complete, the audio data is sent to a server where it is preprocessed and converted by a generative AI model. The converted audio data is then sent back to the robot and played back to the visitor.

[0872] Example prompt sentence:

[0873] "In: Hello, please come in

[0874] Out: Japanese female voice

[0875] These methods allow guidance and instructions within industrial facilities to be given in multiple languages ​​or in the voice of a specific character, thereby achieving efficient communication.

[0876] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0877] Step 1:

[0878] Recording user voice

[0879] When the user presses the recording start button on the robot, the robot's microphone records the voice. The user's voice signal is picked up through the microphone and stored in the device as digital voice data. At this time, the input is the user's voice, and the output is digitized voice data.

[0880] Step 2:

[0881] Sending recording data to the server

[0882] When the user stops recording, the recorded audio data is sent to the server via wireless communication. The HTTPS protocol is used for transmission, ensuring data security. The input here is the recorded audio data, and the output is the data sent to the server.

[0883] Step 3:

[0884] Preprocessing by the server

[0885] The server performs preprocessing on the received audio data. Specifically, it removes noise and normalizes the volume to make the data easier for the generative model to process. The input is the transmitted audio data, and the output is the preprocessed audio data.

[0886] Step 4:

[0887] Voice conversion using generative AI models

[0888] The preprocessed voice data is input to a generative AI model on the server (e.g., Hugging Face's Wav2Vec2), and the voice is converted based on a specified voice profile (e.g., Japanese female voice). An example of a prompt sentence is "In: Hello, please come this way, Out: Japanese female voice." The input is the preprocessed voice data and the voice profile, and the output is the converted voice data.

[0889] Step 5:

[0890] Sending and receiving converted data

[0891] The converted voice data is sent from the server to the robot. The robot's terminal stores the received data and prepares it for playback. The input is the converted voice data, and the output is the data sent to the robot.

[0892] Step 6:

[0893] Playing audio data

[0894] The robot receives the converted data and plays it back through the speaker, allowing users and visitors to listen to the converted audio in real time. The input is the received converted audio data, and the output is the audio that is played back.

[0895] Step 7:

[0896] Viewing Processing Status

[0897] During the conversion process of the voice data, the processing status is displayed on the robot's display and in the mobile application. The user can check the progress. The input is the processing status information, and the output is the status display.

[0898] 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.

[0899] System Overview

[0900] The system is a service that records a user's voice and converts it into a different voice, and also incorporates an emotion engine that recognizes the user's emotions and reflects them in the converted voice. It consists of the following components:

[0901] A means of recording the user's voice

[0902] A means of sending recorded audio data to a server

[0903] Preprocessing method for noise reduction and volume normalization of audio data

[0904] A means of inputting preprocessed speech data into a generative model to convert it into a different speech.

[0905] A means of using an emotion engine to recognize a user's emotional state and select or correct a voice profile based on that

[0906] A means of transmitting the converted audio data to the user's device

[0907] A means for playing the received converted audio data

[0908] A means of recording the user's voice

[0909] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved in the device's temporary memory.

[0910] A means of sending recorded audio data to a server

[0911] Once the recording is complete, the user selects the voice profile they want to convert (for example, the voice of a specific character). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[0912] Audio data preprocessing

[0913] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[0914] Emotion recognition by emotion engine

[0915] The server is equipped with an emotion engine that analyzes the user's emotional state from the recorded voice data. Based on this analysis, the voice profile used by the generative model is automatically selected or corrected, enabling more appropriate voice conversion according to the user's emotions.

[0916] Audio conversion processing

[0917] The generative model converts input speech data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic one to reflect that emotion.

[0918] Transmit and play converted audio

[0919] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[0920] When the device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[0921] Specific examples

[0922] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state based on the user's voice. If the user selects the voice of an animated character, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative model then converts the voice based on that profile, and the converted voice is sent to the user's device via the server. As a result, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[0923] By using this system, users can easily convert their own voice into a variety of voices and even convert the voice according to emotions, providing a richer communication and entertainment experience.

[0924] The processing flow will be explained below.

[0925] Step 1:

[0926] The user launches the application on their device and presses the record button to record their voice.

[0927] Step 2:

[0928] The device temporarily stores the recorded audio data in memory.

[0929] Step 3:

[0930] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[0931] Step 4:

[0932] The device sends the recorded audio data and information about the selected audio profile to the server.

[0933] Step 5:

[0934] The server receives the voice data package transmitted from the terminal.

[0935] Step 6:

[0936] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[0937] Step 7:

[0938] The server's emotion engine analyzes the recorded voice data and recognizes the user's emotional state.

[0939] Step 8:

[0940] The server automatically adjusts or selects the voice profile used by the generative model based on the user's emotional state.

[0941] Step 9:

[0942] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[0943] Step 10:

[0944] The server performs volume normalization, adjusting the volume level of the recorded audio data to a consistent standard level.

[0945] Step 11:

[0946] The server prepares the preprocessed audio data to be input into the generative model.

[0947] Step 12:

[0948] The generative model analyzes the preprocessed speech data and converts it into a specified alternative voice based on information provided by the emotion engine.

[0949] Step 13:

[0950] The generative model generates the converted audio data and returns it to the server.

[0951] Step 14:

[0952] The server stores the generated converted voice data in temporary storage.

[0953] Step 15:

[0954] The server then sends the converted audio data to the user's device using encryption technology.

[0955] Step 16:

[0956] The terminal receives the converted voice data from the server and stores it in temporary memory.

[0957] Step 17:

[0958] The user presses the application's play button to play the converted audio data.

[0959] Step 18:

[0960] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[0961] This process allows users to convert their recorded voice into different voices depending on their emotions and see the results in real time. For example, if the user is feeling "happy," the converted voice will reflect that emotion and be created with a happier tone.

[0962] Example 2

[0963] 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."

[0964] Conventional voice conversion systems have difficulty in converting voices that reflect the user's emotions, making it difficult to generate appropriate voices according to emotions. Ensuring the security of voice data and the stability of the voice conversion process are also issues. To solve these problems, a system with the ability to analyze the user's emotions and automatically select or correct a voice profile according to those emotions is needed.

[0965] 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.

[0966] In this invention, the server includes a means for analyzing the user's emotional state and automatically selecting or correcting a voice profile based on the analysis, a preprocessing means for performing noise reduction and volume normalization on recorded voice data, and a means for requesting a generative AI model to convert input voice data into a different voice based on a pre-trained voice profile. This enables voice conversion according to the user's emotions, resulting in more natural and appropriate communication. Data security is also ensured by the communication protocol and data encryption in the storage device.

[0967] "User" refers to the person who uses the system to record audio and play back the converted audio.

[0968] "Terminal" refers to an electronic device (such as a smartphone or PC) that is owned by the user and that records audio and communicates with the server.

[0969] "Server" refers to a computer system that performs preprocessing of voice data sent from a terminal, emotion analysis, voice profile selection, and voice conversion, and then sends the results to the terminal.

[0970] A "voice profile" is a data set that describes the characteristics of a particular voice and is used for voice conversion.

[0971] "Preprocessing" refers to performing processes such as noise removal and volume normalization on recorded audio data to prepare it in a format that is easy for the generative AI model to handle.

[0972] "Generative AI model" refers to an artificial intelligence model used to convert input voice data into a different voice.

[0973] The "emotion engine" refers to a function that analyzes the user's emotional state from recorded voice data and automatically selects and corrects voice profiles based on the results.

[0974] A "communications protocol" refers to a technology that defines the procedures and rules for sending and receiving data over a network.

[0975] "Storage device" refers to hardware and software components for storing data.

[0976] "Encryption" refers to a conversion process that makes the contents of data unreadable to third parties.

[0977] "Noise reduction" refers to the process of removing unwanted background sounds from recorded audio data.

[0978] "Volume normalization" refers to the process of standardizing the volume level of recorded audio data and adjusting it to a certain standard.

[0979] MODE FOR CARRYING OUT THE INVENTION

[0980] This invention is a system that allows a user to record their voice, convert the voice on a server, and play back the converted voice. The system has the function of analyzing the user's emotional state and converting the voice based on the analysis results.

[0981] User operation

[0982] A user installs a dedicated application on a device such as a smartphone or PC. This application has the function of recording audio using the device's microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. The user then selects the voice profile they want to convert through the application's interface. This profile could include, for example, the voice of a specific anime character.

[0983] Data transmission and preprocessing

[0984] Once the user selects a profile, the device sends the recorded audio data and the selected audio profile information to the server. During this process, communication protocols such as HTTPS and TLS are used to ensure data security. Once the server receives the audio data, preprocessing begins. Software such as Adobe Audition or Audacity is used for preprocessing, which involves noise reduction and volume normalization.

[0985] Sentiment analysis and voice profile selection

[0986] Once preprocessing is complete, the voice data is passed to the emotion engine on the server. The emotion engine uses technologies such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state. Based on the analysis results, an appropriate voice profile is automatically selected or corrected. This function enables more natural voice conversion according to the user's emotions.

[0987] Audio data conversion

[0988] Next, based on the results of emotion analysis and voice profile selection, the generative AI model performs the conversion process. This process uses technologies from OpenAI GPT-3 and Google DeepMind, converting the input voice data into the voice of the specified profile. For example, if the user is recognized as excited, the voice is converted to an energetic voice to reflect that emotion.

[0989] Sending and playing converted audio

[0990] The converted voice data is temporarily stored on the server, then re-encrypted and sent to the user's device. When the device receives the converted voice data, a play button appears in the application. By pressing the play button, the converted voice is played back and the user can check the results.

[0991] Specific examples

[0992] For example, a user can record "hello" and input it into the application. If the emotion engine recognizes an excited state in the user's voice, it will convert it into an energetic voice based on the voice profile of the anime character selected by the user. The voice will then be sent to the user's device via the server, and the user will finally be able to hear "hello" in the excited voice of the anime character.

[0993] Prompt Sentence Examples

[0994] Take a recorded voice saying "Hello" and convert it into an energetic voice based on the voice profile of an excited anime character, then send it to the server.

[0995] In this way, the system of the present invention realizes voice conversion that reflects the user's emotional state, providing a more natural and appropriate means of communication.

[0996] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0997] Step 1:

[0998] The user opens a dedicated application on a device such as a smartphone or PC. The user records audio by speaking into the microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. At this point, the input is the user's voice, and the output is the audio data stored on the device.

[0999] Step 2:

[1000] The user uses an interface within the application to select the voice profile they want to convert, for example, the voice of a particular anime character. Based on this selection, the device sends the recorded voice data and the selected voice profile information to the server. At this point, the input is the user's voice data and the selected voice profile, and the output is the voice data and profile information sent to the server.

[1001] Step 3:

[1002] The server performs preprocessing on the audio data it receives. Specifically, it uses software such as Adobe Audition or Audacity to perform noise reduction and volume normalization. Noise reduction detects and removes background noise, and volume normalization adjusts the audio volume to a certain standard. At this point, the input is the raw audio data sent to the server, and the output is the preprocessed audio data.

[1003] Step 4:

[1004] The preprocessed voice data is passed to the emotion engine on the server. The emotion engine uses tools such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state from the voice data. Based on the analysis results, the voice profile used by the generative AI model is automatically selected or corrected. At this point, the input is the preprocessed voice data, and the output is voice profile information based on the user's emotional state.

[1005] Step 5:

[1006] The generative AI model receives preprocessed voice data and the profile specified by the emotion engine as input, and converts the voice data into a different voice as specified. This process uses technologies from OpenAI GPT-3 and Google DeepMind. For example, if the user is recognized as excited, the voice is converted to an energetic one to reflect that emotion. At this point, the input is the preprocessed voice data and voice profile information, and the output is the converted voice data.

[1007] Step 6:

[1008] The server temporarily stores the converted voice data, re-encrypts it, and sends it to the user's device. The communication protocols used here include "HTTPS" and "TLS." The device receives the converted voice data and displays a play button in the application. When the user presses the play button, the converted voice is played back and the result can be confirmed. At this point, the input is the converted voice data, and the output is the voice played back on the user's device.

[1009] (Application example 2)

[1010] 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."

[1011] Conventional speech conversion systems convert speech without considering the user's emotional state, which means they are unable to convert speech appropriately based on the user's emotions. Furthermore, there is a lack of an interface for converting speech that reflects emotions, limiting the user experience. Furthermore, there is a lack of content distribution services that allow users to access speech conversion results in real time.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for recognizing the user's emotional state using an emotion engine and selecting or correcting a voice profile based on the recognition, means for transmitting the converted voice data to the user's terminal, and means for playing back the received converted voice data. This enables appropriate voice conversion that reflects the user's emotions, providing a rich user experience. Furthermore, real-time voice conversion according to the user's emotions can be realized in content distribution services.

[1013] "Means for recording user voice" refers to a function for acquiring the voice uttered by the user as digital voice data via a microphone.

[1014] The "means for transmitting recorded voice data to a server" is a function for uploading recorded voice data to a remote server via a network.

[1015] "Means for inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a function that performs preprocessing such as noise removal and volume normalization on speech data, and then converts it into a different speech using a machine learning model.

[1016] "Means for recognizing a user's emotional state using an emotion engine and selecting or correcting a voice profile based on that" refers to a function for analyzing voice data to identify a user's emotion and selecting or correcting an appropriate voice profile in accordance with that emotion.

[1017] The "means for transmitting converted voice data to the user's terminal" is a function for sending voice data converted by the generative model to the user's terminal via a network.

[1018] The "means for playing the received converted voice data" is a function for playing the converted voice data received by the user's terminal through a playback device.

[1019] The "pre-processing means for noise removal and volume normalization" is a function for removing environmental sounds and unnecessary sounds from recorded audio data and for equalizing variations in volume.

[1020] "Means for converting voice based on a voice profile selected by a generative model" is a function for converting a user's voice data into a target voice using a voice profile that has been pre-trained for voice conversion.

[1021] "Means for selecting from multiple types" is a function that provides an interface that allows users to select the audio profile they want to use from multiple options.

[1022] "Means for communication protocols and storage to ensure data security using encryption technology" refers to a function that applies encryption technology during communication and storage to maintain data security and prevent unauthorized access to voice data and other information.

[1023] System Overview

[1024] This invention is a service that records a user's voice and converts it into a different voice, and is a system that uses an emotion engine to recognize the user's emotions and reflect them in the converted voice. This system converts voices according to the user's emotions, allowing for richer communication and entertainment.

[1025] Components and Functions

[1026] 1. How to record your voice:

[1027] The microphone on a smartphone or computer is used to record the user's voice. Through a dedicated application, the user can record the voice and start recording by pressing the record button.

[1028] 2. How to send recorded audio data to the server:

[1029] Once the recording is complete, the user selects the voice profile they want to convert. After selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server. Data transmission is via the Internet.

[1030] 3. Preprocessing the audio data:

[1031] When the server receives the audio data, it first performs preprocessing such as noise reduction and volume normalization, and then inputs the preprocessed audio data into the generative AI model.

[1032] 4. Emotion Recognition with Emotion Engine:

[1033] The server is equipped with an emotion engine that analyzes the user's emotional state from recorded voice data. Based on this analysis, the generative model automatically selects or corrects the voice profile used, enabling voice conversion according to the user's emotions.

[1034] 5. Audio conversion process:

[1035] The generative AI model converts input voice data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic voice to reflect that emotion.

[1036] 6. Transmit and play converted audio:

[1037] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security. When the converted voice data is received by the device, a play button appears in the application, allowing the user to play back the converted voice and check the results.

[1038] Hardware and software used

[1039] Hardware:

[1040] Smartphone or computer microphone

[1041] software:

[1042] Dedicated application

[1043] A rigged emotional engine

[1044] Audio preprocessing and generative AI models

[1045] Encrypted communication protocols

[1046] Specific examples

[1047] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state from the user's voice. The user selects the voice of an animated character. In this case, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative AI model converts the voice based on that profile, and the converted voice is sent to the user's device via the server. Finally, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[1048] Prompt Sentence Examples

[1049] User's speech: "Hello"

[1050] Recognized emotion: Excitement

[1051] Selected voice profile: Energetic anime character

[1052] As described above, using this system makes it possible to convert voice according to the user's emotions, providing a richer communication experience that reflects emotions in real time.

[1053] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1054] Step 1:

[1055] The user records audio using a dedicated application. The user uses the microphone on their smartphone or computer to input audio by pressing the record button on the application. When recording is complete, the audio data is saved in temporary memory. The input is the user's audio data, and the output is the digital audio data saved in temporary memory.

[1056] Step 2:

[1057] The device sends the recorded voice data to the server. The user selects the voice profile they want to convert and presses the send button, which uploads the voice data and profile information to the server. The input is the recorded voice data and the selected voice profile, and the output is the data uploaded to the server.

[1058] Step 3:

[1059] The server performs preprocessing on the received audio data. Preprocessing includes noise removal and volume normalization, which converts the data into a form that is easy for the generative AI model to process. The input is the audio data uploaded to the server, and the output is preprocessed audio data that has been noise removed and volume normalized.

[1060] Step 4:

[1061] The server inputs the preprocessed speech data into an emotion engine to recognize the user's emotional state. The emotion engine analyzes the speech characteristics to identify the user's emotion (e.g., excitement, sadness, joy). The input is the preprocessed speech data, and the output is the recognized emotional state.

[1062] Step 5:

[1063] The server selects an appropriate voice profile for the generative AI model based on the output of the emotion engine (recognized emotional state). The profile is automatically applied depending on the emotion. The input is the emotional state and available voice profiles, and the output is the selected or corrected voice profile.

[1064] Step 6:

[1065] The server uses a generative AI model to convert the preprocessed voice data based on the selected voice profile. The generative AI model converts the voice into a new voice based on the training data. The input is the preprocessed voice data and the selected voice profile, and the output is the converted voice data.

[1066] Step 7:

[1067] The server sends the converted voice data to the user's device. Data security is ensured using encrypted communication. The input is the converted voice data, and the output is the voice data sent to the user's device.

[1068] Step 8:

[1069] The converted voice data received by the device is played back. A play button is displayed on the application, and the user can press it to play back the converted voice. The input is the voice data sent to the device, and the output is the voice played back on the application.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] [Fourth embodiment]

[1074] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1075] 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.

[1076] 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).

[1077] 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.

[1078] 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.

[1079] 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).

[1080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] 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.

[1085] 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.

[1086] 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."

[1087] System Overview

[1088] The system is a service that allows users to record their own voice and convert it into a different voice, and consists of the following components:

[1089] A means of recording the user's voice

[1090] A means of sending recorded audio data to a server

[1091] A means to preprocess speech data on the server and input it into a generative model

[1092] A means of returning the audio converted by the generative model to the user

[1093] A means to play the converted audio

[1094] A means of recording the user's voice

[1095] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved on the device.

[1096] A means of sending recorded audio data to a server

[1097] Once the recording is complete, the user selects the voice profile they want to convert (e.g., the voice of the opposite sex or a specific voice actor). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[1098] A means to preprocess speech data on the server and input it into a generative model

[1099] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[1100] A means of converting audio using a generative model

[1101] A generative model converts input audio data into a specified voice based on a pre-trained voice profile, for example, converting a male voice into a female voice or changing the voice of a specific character.

[1102] A means of returning the converted audio to the user

[1103] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[1104] A means to play the converted audio

[1105] Once the user's device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[1106] Specific examples

[1107] For example, consider a case where a user records "Good morning" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "Good morning" is played in the anime character's voice.

[1108] By using this system, users can easily convert their own voice into a variety of voices, allowing them to experience new forms of enjoyment in communication and entertainment settings.

[1109] The processing flow will be explained below.

[1110] Step 1:

[1111] The user launches the application on their device and presses the record button to record their voice.

[1112] Step 2:

[1113] The device temporarily stores the recorded audio data in memory.

[1114] Step 3:

[1115] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[1116] Step 4:

[1117] The device packages the selected audio profile and the recorded audio data and sends it to the server.

[1118] Step 5:

[1119] The server receives the voice data package transmitted from the terminal.

[1120] Step 6:

[1121] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[1122] Step 7:

[1123] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[1124] Step 8:

[1125] The server performs a volume normalization process to adjust the volume level of the audio to a consistent standard.

[1126] Step 9:

[1127] The server prepares the preprocessed audio data to be input into the generative model.

[1128] Step 10:

[1129] A generative model analyzes the preprocessed audio data and transforms the audio based on a specified audio profile.

[1130] Step 11:

[1131] The generative model generates the converted audio data and returns it to the server.

[1132] Step 12:

[1133] The server stores the generated converted voice data in temporary storage.

[1134] Step 13:

[1135] The server then sends the converted audio data to the user's device using encryption technology.

[1136] Step 14:

[1137] The terminal receives the converted voice data from the server and stores it in temporary memory.

[1138] Step 15:

[1139] The user presses the application's play button to play the converted audio data.

[1140] Step 16:

[1141] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[1142] This processing flow allows users to easily convert their recorded voice into a different voice and see the results in real time.

[1143] Example 1

[1144] 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."

[1145] In today's world, there is a growing need for users to convert their own voice into that of another person or a specific character. However, conventional systems often lack the accuracy and processing speed of voice conversion, resulting in a poor user experience. Furthermore, data security is often not fully considered, creating privacy issues.

[1146] 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.

[1147] In this invention, the server includes a means for recording the user's voice, a means for transmitting the recorded voice data to the server, a means for preprocessing the voice data on the server and inputting the voice into a generative model to convert it into a different voice, a means for transmitting the converted voice data to the user's device, and a means for playing back the received converted voice data. This achieves high conversion accuracy and processing speed, allowing the user to experience fast and accurate voice conversion. In addition, the use of encryption technology ensures data security and protects the user's privacy.

[1148] "Means for recording the user's voice" refers to the means by which a user records voice through a microphone using a device such as a smartphone or computer.

[1149] The "means for transmitting recorded voice data to the server" refers to a means for transmitting voice data and related information from the user's terminal to the server.

[1150] "Means for preprocessing audio data on a server and inputting the audio into a generative model to convert it into a different audio" refers to a means for a server to receive audio data, perform preprocessing such as noise removal and volume normalization, and then use a generative AI model to convert the audio into a different specified audio.

[1151] The "means for transmitting converted voice data to the user's terminal" refers to a means for transmitting converted voice data from the server to the user's terminal.

[1152] The "means for reproducing the received converted voice data" refers to a means for reproducing the received voice data on the user's terminal.

[1153] "Denoising" is the process of removing unwanted background noise from audio data.

[1154] "Volume normalization" is the process of adjusting the volume level of audio data to a uniform level.

[1155] A "generative model" is an artificial intelligence model for converting speech into another speech based on a learned profile.

[1156] A "voice profile" is a set of pre-defined voice characteristic parameters that determine the characteristics of the converted voice.

[1157] "Encryption technology" is a technology for ensuring data security in communication protocols and storage.

[1158] System Overview

[1159] The system provides technology that allows users to record their own voice and convert it into a different voice. The system includes the following elements:

[1160] A means of recording the user's voice

[1161] A means of sending recorded audio data to a server

[1162] A means to preprocess speech data on the server and input it into a generative model

[1163] A means of converting speech using a generative model

[1164] A means of transmitting the converted audio data to the user's device

[1165] A means of playing the converted audio data

[1166] A means of recording the user's voice

[1167] Users use a dedicated application on their smartphone, PC, or other device to record their own voice using the microphone. Pressing the record button starts recording, and pressing the stop button saves the audio data on the device. For example, if a user says "Good morning," this voice will be recorded.

[1168] A means of sending recorded audio data to a server

[1169] Once the recording is complete, the user selects the voice profile they want to convert within the application, such as "female voice" or "anime character voice." The device then sends the recorded voice data and information about the selected voice profile to the server. The communication is encrypted using the HTTPS protocol.

[1170] A means to preprocess speech data on the server and input it into a generative model

[1171] When the server receives the audio data, it first performs preprocessing such as noise removal and volume normalization. This makes the audio data easier for the generative model to process. For example, it filters out background noise and adjusts volume peaks. The preprocessed audio data is then input into the generative model.

[1172] A means of converting audio using a generative model

[1173] The server inputs the preprocessed voice data into a generative AI model, which converts the voice based on the specified voice profile. For example, to convert a male voice into a female voice, it adjusts the frequency and timbre. The generative model uses a neural network, enabling highly accurate voice conversion.

[1174] A means of transmitting the converted audio data to the user's device

[1175] The converted audio data is temporarily stored on the server and then promptly sent to the user's device, also encrypted using the HTTPS protocol.

[1176] A means to play the converted audio

[1177] When the user's device receives the converted voice data, a play button will appear in the dedicated application. When the user presses the play button, the converted voice will be played and the user can check the results. For example, "Good morning" will be played in the voice of an anime character.

[1178] Examples of concrete examples and prompts

[1179] For example, consider a case where a user records "hello" and wants to convert it into the voice of a specific anime character. After recording, the user selects the anime character's voice and sends it to the server. The server preprocesses the audio and converts it using a generative model. The converted audio is sent back to the user's device, and when the user presses the play button, "hello" is played in the anime character's voice.

[1180] Examples of prompts used in this system include "Please convert this voice into a female voice" and "Please convert the recorded voice into the voice of an anime character."

[1181] With the above-described configuration and procedures, this system is able to convert a user's voice into another voice with high accuracy and provide it quickly and safely.

[1182] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1183] Step 1: Record the user's voice

[1184] The user launches a dedicated application and records their own voice using the microphone on their smartphone or computer. When the user taps the record button, voice recording begins, and the voice data up until the end of the recording is saved in the device's temporary memory. When the user taps the stop button, recording ends, and the voice data is saved in the device's storage. The user's voice is given as input, and the recorded voice data is saved as output. For example, the user might say, "Good morning."

[1185] Step 2: Send the recorded audio data to the server

[1186] Once the recording is complete, the user selects the voice profile they want to convert to within the application, for example, "female voice" or "anime character voice." After that, by tapping the send button, the device will send the recorded voice data and the selected voice profile information to the server, encrypting it using the HTTPS protocol. The recorded voice data and the selected voice profile are given as input, and sent to the server as output.

[1187] Step 3: Preprocess the audio data on the server

[1188] When the server receives the audio data, it performs preprocessing such as noise removal and volume normalization. Specifically, it filters out background noise and evens out volume peaks. Recorded audio data is given as input, and preprocessed audio data is generated as output. This converts the data into a format that is easy for the generative AI model to process.

[1189] Step 4: Input audio to the generative AI model and perform conversion

[1190] The preprocessed voice data is input into a generative AI model, which converts it into a different voice based on a specified voice profile. The generative AI model utilizes a trained neural network to convert a male voice into a female voice, or into the voice of a specific character. The preprocessed voice data and voice profile are given as input, and the converted voice data is generated as output. For example, "Good morning" is converted into a female voice.

[1191] Step 5: Send the converted audio data to the user's device

[1192] The converted voice data is temporarily stored on the server and then encrypted using the HTTPS protocol before being sent to the user's device. The converted voice data is given as input and sent to the user's device as output.

[1193] Step 6: Play the converted audio

[1194] When the user's device receives the converted voice data, a play button is displayed within the dedicated application. When the user presses the play button, the converted voice is played. The converted voice data is given as input, and the voice is played as output. For example, when the user presses the play button, "Good morning" is played in the voice of an anime character.

[1195] Through the above processing steps, users can convert their own voice with high accuracy based on various profiles and enjoy it quickly and safely.

[1196] (Application example 1)

[1197] 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."

[1198] Industrial facilities require guidance in multiple languages ​​or with specific character voices, but current systems only support one language and have limited guidance methods. Furthermore, users have limited means of understanding the progress of the process of converting recorded voices into different voices, making efficient communication difficult. Furthermore, robots that provide guidance in industrial facilities require advanced technology to convert voices in real time, but current technology makes this difficult.

[1199] 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.

[1200] In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for transmitting the converted voice data to the user's terminal, means for playing back the received converted voice data, means for converting the voice in the robot using a specific voice profile to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and means for displaying the processing status until the recorded voice data is converted. This makes it possible to provide guidance and instructions within the industrial facility in multiple languages ​​or in the voice of a specific character, and allows users (e.g., visitors or workers) to understand the progress of the voice conversion in real time, thereby achieving efficient communication.

[1201] "Means for recording the user's voice" means means including a microphone or recording device that a user uses to record their voice.

[1202] The "means for transmitting recorded voice data to a server" refers to a means for sending recorded voice data to a server via a network.

[1203] "The effort of inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a method of inputting speech data that has been preprocessed, such as by noise removal and volume normalization, into a generative model and converting it into a specified different speech.

[1204] The "means for transmitting converted voice data to a user's terminal" refers to a means for transmitting voice data converted by a generative model to a user's terminal via a network.

[1205] The "means for reproducing the received converted voice data" refers to a means for reproducing the converted voice data received at the user's terminal.

[1206] "Means for converting voice using a specific voice profile and providing guidance or instructions within an industrial facility in multiple languages ​​or in the voice of a specific character" refers to means for converting voice using a specific voice profile and providing information in different languages ​​or in the voice of a specific character when providing guidance or instructions within an industrial facility.

[1207] The "means for displaying the processing status until the recorded audio data is converted" is a means for displaying to the user the progress status until the conversion process is completed.

[1208] The system for implementing this invention records the user's voice, converts it into a different voice, and provides guidance and instructions within an industrial facility. The overall configuration and operation of the system will be described in detail below.

[1209] System Overview

[1210] The system consists of the following components:

[1211] How to record user voice: The user can record their own voice using the microphone installed on the robot. Recording starts when the start button is pressed, and ends when the stop button is pressed.

[1212] A method for transmitting recorded audio data to a server: Once recording is complete, the audio data is transmitted via wireless communication to a server, which receives the data securely using Internet protocols (e.g., HTTPS).

[1213] A means of inputting preprocessed audio data into a generative model: The server performs preprocessing (noise removal and volume normalization) on the received audio data. The preprocessed data is input into a generative AI model, where it is converted based on a specified audio profile. An example of this generative AI model is Hugging Face's Wav2Vec2.

[1214] A method for sending the converted voice data to the user's terminal: The converted voice data is sent from the server to the robot, which receives the voice data, checks it, and plays it back.

[1215] A means for playing the received converted voice data: The robot is equipped with a speaker and plays the converted voice, allowing it to provide guidance to users and visitors in multiple languages ​​or with the voices of different characters.

[1216] A means of displaying the processing status while the recorded audio data is being converted: The user can check the progress of the recording and conversion on the robot's display or in the mobile application.

[1217] Examples of technologies used

[1218] Hardware: A robot with a microphone for recording audio, a speaker for playing audio, and a network interface to support wireless communication.

[1219] Software: The application that controls the audio recording and playback uses the sounddevice library. For audio conversion, we use a generative AI model (e.g., Hugging Face's Wav2Vec2). The entire data processing and communication uses the secure HTTP communication protocol (HTTPS).

[1220] Specific examples

[1221] For example, consider a scenario where a guide robot in a factory needs to guide visitors, saying "Hello, please come this way." The user records this message through a microphone and selects a specific voice profile (e.g., a female Japanese voice). Once the recording is complete, the audio data is sent to a server where it is preprocessed and converted by a generative AI model. The converted audio data is then sent back to the robot and played back to the visitor.

[1222] Example prompt sentence:

[1223] "In: Hello, please come in

[1224] Out: Japanese female voice

[1225] These methods allow guidance and instructions within industrial facilities to be given in multiple languages ​​or in the voice of a specific character, thereby achieving efficient communication.

[1226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1227] Step 1:

[1228] Recording user voice

[1229] When the user presses the recording start button on the robot, the robot's microphone records the voice. The user's voice signal is picked up through the microphone and stored in the device as digital voice data. At this time, the input is the user's voice, and the output is digitized voice data.

[1230] Step 2:

[1231] Sending recording data to the server

[1232] When the user stops recording, the recorded audio data is sent to the server via wireless communication. The HTTPS protocol is used for transmission, ensuring data security. The input here is the recorded audio data, and the output is the data sent to the server.

[1233] Step 3:

[1234] Preprocessing by the server

[1235] The server performs preprocessing on the received audio data. Specifically, it removes noise and normalizes the volume to make the data easier for the generative model to process. The input is the transmitted audio data, and the output is the preprocessed audio data.

[1236] Step 4:

[1237] Voice conversion using generative AI models

[1238] The preprocessed voice data is input to a generative AI model on the server (e.g., Hugging Face's Wav2Vec2), and the voice is converted based on a specified voice profile (e.g., Japanese female voice). An example of a prompt sentence is "In: Hello, please come this way, Out: Japanese female voice." The input is the preprocessed voice data and the voice profile, and the output is the converted voice data.

[1239] Step 5:

[1240] Sending and receiving converted data

[1241] The converted voice data is sent from the server to the robot. The robot's terminal stores the received data and prepares it for playback. The input is the converted voice data, and the output is the data sent to the robot.

[1242] Step 6:

[1243] Playing audio data

[1244] The robot receives the converted data and plays it back through the speaker, allowing users and visitors to listen to the converted audio in real time. The input is the received converted audio data, and the output is the audio that is played back.

[1245] Step 7:

[1246] Viewing Processing Status

[1247] During the conversion process of the voice data, the processing status is displayed on the robot's display and in the mobile application. The user can check the progress. The input is the processing status information, and the output is the status display.

[1248] 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.

[1249] System Overview

[1250] The system is a service that records a user's voice and converts it into a different voice, and also incorporates an emotion engine that recognizes the user's emotions and reflects them in the converted voice. It consists of the following components:

[1251] A means of recording the user's voice

[1252] A means of sending recorded audio data to a server

[1253] Preprocessing method for noise reduction and volume normalization of audio data

[1254] A means of inputting preprocessed speech data into a generative model to convert it into a different speech.

[1255] A means of using an emotion engine to recognize a user's emotional state and select or correct a voice profile based on that

[1256] A means of transmitting the converted audio data to the user's device

[1257] A means for playing the received converted audio data

[1258] A means of recording the user's voice

[1259] Users use a dedicated application on their smartphone, PC, or other device to record their voice through the microphone. By pressing the record button, audio recording begins, and when it stops, the audio data is saved in the device's temporary memory.

[1260] A means of sending recorded audio data to a server

[1261] Once the recording is complete, the user selects the voice profile they want to convert (for example, the voice of a specific character). After the selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server.

[1262] Audio data preprocessing

[1263] When the server receives the audio data, it first performs preprocessing. Specifically, it makes the data easier for the generative model to process through processes such as noise removal and volume normalization. The preprocessed audio data is then input into the generative AI model.

[1264] Emotion recognition by emotion engine

[1265] The server is equipped with an emotion engine that analyzes the user's emotional state from the recorded voice data. Based on this analysis, the voice profile used by the generative model is automatically selected or corrected, enabling more appropriate voice conversion according to the user's emotions.

[1266] Audio conversion processing

[1267] The generative model converts input speech data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic one to reflect that emotion.

[1268] Transmit and play converted audio

[1269] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security.

[1270] When the device receives the converted voice data, a play button will appear in the application, allowing the user to play back the converted voice and check the results.

[1271] Specific examples

[1272] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state based on the user's voice. If the user selects the voice of an animated character, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative model then converts the voice based on that profile, and the converted voice is sent to the user's device via the server. As a result, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[1273] By using this system, users can easily convert their own voice into a variety of voices and even convert the voice according to emotions, providing a richer communication and entertainment experience.

[1274] The processing flow will be explained below.

[1275] Step 1:

[1276] The user launches the application on their device and presses the record button to record their voice.

[1277] Step 2:

[1278] The device temporarily stores the recorded audio data in memory.

[1279] Step 3:

[1280] The user selects the voice profile they want to convert within the application (e.g., the voice of a particular character).

[1281] Step 4:

[1282] The device sends the recorded audio data and information about the selected audio profile to the server.

[1283] Step 5:

[1284] The server receives the voice data package transmitted from the terminal.

[1285] Step 6:

[1286] The server analyzes the metadata contained in the audio data (user ID, selected voice profile, etc.).

[1287] Step 7:

[1288] The server's emotion engine analyzes the recorded voice data and recognizes the user's emotional state.

[1289] Step 8:

[1290] The server automatically adjusts or selects the voice profile used by the generative model based on the user's emotional state.

[1291] Step 9:

[1292] The server performs noise reduction processing on the received audio data to remove unnecessary background sounds and noise.

[1293] Step 10:

[1294] The server performs volume normalization, adjusting the volume level of the recorded audio data to a consistent standard level.

[1295] Step 11:

[1296] The server prepares the preprocessed audio data to be input into the generative model.

[1297] Step 12:

[1298] The generative model analyzes the preprocessed speech data and converts it into a specified alternative voice based on information provided by the emotion engine.

[1299] Step 13:

[1300] The generative model generates the converted audio data and returns it to the server.

[1301] Step 14:

[1302] The server stores the generated converted voice data in temporary storage.

[1303] Step 15:

[1304] The server then sends the converted audio data to the user's device using encryption technology.

[1305] Step 16:

[1306] The terminal receives the converted voice data from the server and stores it in temporary memory.

[1307] Step 17:

[1308] The user presses the application's play button to play the converted audio data.

[1309] Step 18:

[1310] The device plays the converted audio to the user through the built-in speaker or an external speaker.

[1311] This process allows users to convert their recorded voice into different voices depending on their emotions and see the results in real time. For example, if the user is feeling "happy," the converted voice will reflect that emotion and be created with a happier tone.

[1312] Example 2

[1313] 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."

[1314] Conventional voice conversion systems have difficulty in converting voices that reflect the user's emotions, making it difficult to generate appropriate voices according to emotions. Ensuring the security of voice data and the stability of the voice conversion process are also issues. To solve these problems, a system with the ability to analyze the user's emotions and automatically select or correct a voice profile according to those emotions is needed.

[1315] 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.

[1316] In this invention, the server includes a means for analyzing the user's emotional state and automatically selecting or correcting a voice profile based on the analysis, a preprocessing means for performing noise reduction and volume normalization on recorded voice data, and a means for requesting a generative AI model to convert input voice data into a different voice based on a pre-trained voice profile. This enables voice conversion according to the user's emotions, resulting in more natural and appropriate communication. Data security is also ensured by the communication protocol and data encryption in the storage device.

[1317] "User" refers to the person who uses the system to record audio and play back the converted audio.

[1318] "Terminal" refers to an electronic device (such as a smartphone or PC) that is owned by the user and that records audio and communicates with the server.

[1319] "Server" refers to a computer system that performs preprocessing of voice data sent from a terminal, emotion analysis, voice profile selection, and voice conversion, and then sends the results to the terminal.

[1320] A "voice profile" is a data set that describes the characteristics of a particular voice and is used for voice conversion.

[1321] "Preprocessing" refers to performing processes such as noise removal and volume normalization on recorded audio data to prepare it in a format that is easy for the generative AI model to handle.

[1322] "Generative AI model" refers to an artificial intelligence model used to convert input voice data into a different voice.

[1323] The "emotion engine" refers to a function that analyzes the user's emotional state from recorded voice data and automatically selects and corrects voice profiles based on the results.

[1324] A "communications protocol" refers to a technology that defines the procedures and rules for sending and receiving data over a network.

[1325] "Storage device" refers to hardware and software components for storing data.

[1326] "Encryption" refers to a conversion process that makes the contents of data unreadable to third parties.

[1327] "Noise reduction" refers to the process of removing unwanted background sounds from recorded audio data.

[1328] "Volume normalization" refers to the process of standardizing the volume level of recorded audio data and adjusting it to a certain standard.

[1329] MODE FOR CARRYING OUT THE INVENTION

[1330] This invention is a system that allows a user to record their voice, convert the voice on a server, and play back the converted voice. The system has the function of analyzing the user's emotional state and converting the voice based on the analysis results.

[1331] User operation

[1332] A user installs a dedicated application on a device such as a smartphone or PC. This application has the function of recording audio using the device's microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. The user then selects the voice profile they want to convert through the application's interface. This profile could include, for example, the voice of a specific anime character.

[1333] Data transmission and preprocessing

[1334] Once the user selects a profile, the device sends the recorded audio data and the selected audio profile information to the server. During this process, communication protocols such as HTTPS and TLS are used to ensure data security. Once the server receives the audio data, preprocessing begins. Software such as Adobe Audition or Audacity is used for preprocessing, which involves noise reduction and volume normalization.

[1335] Sentiment analysis and voice profile selection

[1336] Once preprocessing is complete, the voice data is passed to the emotion engine on the server. The emotion engine uses technologies such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state. Based on the analysis results, an appropriate voice profile is automatically selected or corrected. This function enables more natural voice conversion according to the user's emotions.

[1337] Audio data conversion

[1338] Next, based on the results of emotion analysis and voice profile selection, the generative AI model performs the conversion process. This process uses technologies from OpenAI GPT-3 and Google DeepMind, converting the input voice data into the voice of the specified profile. For example, if the user is recognized as excited, the voice is converted to an energetic voice to reflect that emotion.

[1339] Sending and playing converted audio

[1340] The converted voice data is temporarily stored on the server, then re-encrypted and sent to the user's device. When the device receives the converted voice data, a play button appears in the application. By pressing the play button, the converted voice is played back and the user can check the results.

[1341] Specific examples

[1342] For example, a user can record "hello" and input it into the application. If the emotion engine recognizes an excited state in the user's voice, it will convert it into an energetic voice based on the voice profile of the anime character selected by the user. The voice will then be sent to the user's device via the server, and the user will finally be able to hear "hello" in the excited voice of the anime character.

[1343] Prompt Sentence Examples

[1344] Take a recorded voice saying "Hello" and convert it into an energetic voice based on the voice profile of an excited anime character, then send it to the server.

[1345] In this way, the system of the present invention realizes voice conversion that reflects the user's emotional state, providing a more natural and appropriate means of communication.

[1346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1347] Step 1:

[1348] The user opens a dedicated application on a device such as a smartphone or PC. The user records audio by speaking into the microphone. When the user presses the record button, the device collects the audio data and stores it in temporary memory. At this point, the input is the user's voice, and the output is the audio data stored on the device.

[1349] Step 2:

[1350] The user uses an interface within the application to select the voice profile they want to convert, for example, the voice of a particular anime character. Based on this selection, the device sends the recorded voice data and the selected voice profile information to the server. At this point, the input is the user's voice data and the selected voice profile, and the output is the voice data and profile information sent to the server.

[1351] Step 3:

[1352] The server performs preprocessing on the audio data it receives. Specifically, it uses software such as Adobe Audition or Audacity to perform noise reduction and volume normalization. Noise reduction detects and removes background noise, and volume normalization adjusts the audio volume to a certain standard. At this point, the input is the raw audio data sent to the server, and the output is the preprocessed audio data.

[1353] Step 4:

[1354] The preprocessed voice data is passed to the emotion engine on the server. The emotion engine uses tools such as IBM Watson and Microsoft Azure Emotion API to analyze the user's emotional state from the voice data. Based on the analysis results, the voice profile used by the generative AI model is automatically selected or corrected. At this point, the input is the preprocessed voice data, and the output is voice profile information based on the user's emotional state.

[1355] Step 5:

[1356] The generative AI model receives preprocessed voice data and the profile specified by the emotion engine as input, and converts the voice data into a different voice as specified. This process uses technologies from OpenAI GPT-3 and Google DeepMind. For example, if the user is recognized as excited, the voice is converted to an energetic one to reflect that emotion. At this point, the input is the preprocessed voice data and voice profile information, and the output is the converted voice data.

[1357] Step 6:

[1358] The server temporarily stores the converted voice data, re-encrypts it, and sends it to the user's device. The communication protocols used here include "HTTPS" and "TLS." The device receives the converted voice data and displays a play button in the application. When the user presses the play button, the converted voice is played back and the result can be confirmed. At this point, the input is the converted voice data, and the output is the voice played back on the user's device.

[1359] (Application example 2)

[1360] 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."

[1361] Conventional speech conversion systems convert speech without considering the user's emotional state, which means they are unable to convert speech appropriately based on the user's emotions. Furthermore, there is a lack of an interface for converting speech that reflects emotions, limiting the user experience. Furthermore, there is a lack of content distribution services that allow users to access speech conversion results in real time.

[1362] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording the user's voice, means for transmitting the recorded voice data to the server, means for inputting the preprocessed voice data into a generative model and converting it into a different voice, means for recognizing the user's emotional state using an emotion engine and selecting or correcting a voice profile based on the recognition, means for transmitting the converted voice data to the user's terminal, and means for playing back the received converted voice data. This enables appropriate voice conversion that reflects the user's emotions, providing a rich user experience. Furthermore, real-time voice conversion according to the user's emotions can be realized in content distribution services.

[1363] "Means for recording user voice" refers to a function for acquiring the voice uttered by the user as digital voice data via a microphone.

[1364] The "means for transmitting recorded voice data to a server" is a function for uploading recorded voice data to a remote server via a network.

[1365] "Means for inputting preprocessed speech data into a generative model and converting it into a different speech" refers to a function that performs preprocessing such as noise removal and volume normalization on speech data, and then converts it into a different speech using a machine learning model.

[1366] "Means for recognizing a user's emotional state using an emotion engine and selecting or correcting a voice profile based on that" refers to a function for analyzing voice data to identify a user's emotion and selecting or correcting an appropriate voice profile in accordance with that emotion.

[1367] The "means for transmitting converted voice data to the user's terminal" is a function for sending voice data converted by the generative model to the user's terminal via a network.

[1368] The "means for playing the received converted voice data" is a function for playing the converted voice data received by the user's terminal through a playback device.

[1369] The "pre-processing means for noise removal and volume normalization" is a function for removing environmental sounds and unnecessary sounds from recorded audio data and for equalizing variations in volume.

[1370] "Means for converting voice based on a voice profile selected by a generative model" is a function for converting a user's voice data into a target voice using a voice profile that has been pre-trained for voice conversion.

[1371] "Means for selecting from multiple types" is a function that provides an interface that allows users to select the audio profile they want to use from multiple options.

[1372] "Means for communication protocols and storage to ensure data security using encryption technology" refers to a function that applies encryption technology during communication and storage to maintain data security and prevent unauthorized access to voice data and other information.

[1373] System Overview

[1374] This invention is a service that records a user's voice and converts it into a different voice, and is a system that uses an emotion engine to recognize the user's emotions and reflect them in the converted voice. This system converts voices according to the user's emotions, allowing for richer communication and entertainment.

[1375] Components and Functions

[1376] 1. How to record your voice:

[1377] The microphone on a smartphone or computer is used to record the user's voice. Through a dedicated application, the user can record the voice and start recording by pressing the record button.

[1378] 2. How to send recorded audio data to the server:

[1379] Once the recording is complete, the user selects the voice profile they want to convert. After selection is complete, the device sends the recorded voice data and information about the selected voice profile to the server. Data transmission is via the Internet.

[1380] 3. Preprocessing the audio data:

[1381] When the server receives the audio data, it first performs preprocessing such as noise reduction and volume normalization, and then inputs the preprocessed audio data into the generative AI model.

[1382] 4. Emotion Recognition with Emotion Engine:

[1383] The server is equipped with an emotion engine that analyzes the user's emotional state from recorded voice data. Based on this analysis, the generative model automatically selects or corrects the voice profile used, enabling voice conversion according to the user's emotions.

[1384] 5. Audio conversion process:

[1385] The generative AI model converts input voice data into a specified voice based on a pre-trained voice profile. For example, if the user is recognized as excited, the model can convert the voice into a more energetic voice to reflect that emotion.

[1386] 6. Transmit and play converted audio:

[1387] The converted voice data is temporarily stored on the server and then promptly sent to the user's device. The communication protocol uses encryption technology to ensure data security. When the converted voice data is received by the device, a play button appears in the application, allowing the user to play back the converted voice and check the results.

[1388] Hardware and software used

[1389] Hardware:

[1390] Smartphone or computer microphone

[1391] software:

[1392] Dedicated application

[1393] A rigged emotional engine

[1394] Audio preprocessing and generative AI models

[1395] Encrypted communication protocols

[1396] Specific examples

[1397] For example, consider the case where a user records "hello" and the emotion engine recognizes an excited state from the user's voice. The user selects the voice of an animated character. In this case, the emotion engine automatically selects a more energetic voice profile according to the excited state. The generative AI model converts the voice based on that profile, and the converted voice is sent to the user's device via the server. Finally, the user receives a recording of "hello" played back in the voice of an animated character that reflects the excited emotion.

[1398] Prompt Sentence Examples

[1399] User's speech: "Hello"

[1400] Recognized emotion: Excitement

[1401] Selected voice profile: Energetic anime character

[1402] As described above, using this system makes it possible to convert voice according to the user's emotions, providing a richer communication experience that reflects emotions in real time.

[1403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1404] Step 1:

[1405] The user records audio using a dedicated application. The user uses the microphone on their smartphone or computer to input audio by pressing the record button on the application. When recording is complete, the audio data is saved in temporary memory. The input is the user's audio data, and the output is the digital audio data saved in temporary memory.

[1406] Step 2:

[1407] The device sends the recorded voice data to the server. The user selects the voice profile they want to convert and presses the send button, which uploads the voice data and profile information to the server. The input is the recorded voice data and the selected voice profile, and the output is the data uploaded to the server.

[1408] Step 3:

[1409] The server performs preprocessing on the received audio data. Preprocessing includes noise removal and volume normalization, which converts the data into a form that is easy for the generative AI model to process. The input is the audio data uploaded to the server, and the output is preprocessed audio data that has been noise removed and volume normalized.

[1410] Step 4:

[1411] The server inputs the preprocessed speech data into an emotion engine to recognize the user's emotional state. The emotion engine analyzes the speech characteristics to identify the user's emotion (e.g., excitement, sadness, joy). The input is the preprocessed speech data, and the output is the recognized emotional state.

[1412] Step 5:

[1413] The server selects an appropriate voice profile for the generative AI model based on the output of the emotion engine (recognized emotional state). The profile is automatically applied depending on the emotion. The input is the emotional state and available voice profiles, and the output is the selected or corrected voice profile.

[1414] Step 6:

[1415] The server uses a generative AI model to convert the preprocessed voice data based on the selected voice profile. The generative AI model converts the voice into a new voice based on the training data. The input is the preprocessed voice data and the selected voice profile, and the output is the converted voice data.

[1416] Step 7:

[1417] The server sends the converted voice data to the user's device. Data security is ensured using encrypted communication. The input is the converted voice data, and the output is the voice data sent to the user's device.

[1418] Step 8:

[1419] The converted voice data received by the device is played back. A play button is displayed on the application, and the user can press it to play back the converted voice. The input is the voice data sent to the device, and the output is the voice played back on the application.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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).

[1427] 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.

[1428] 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."

[1429] 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.

[1430] 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).

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] The following is further disclosed regarding the above embodiment.

[1442] (Claim 1)

[1443] a means for recording the user's voice;

[1444] means for transmitting the recorded voice data to a server;

[1445] A means for inputting the preprocessed speech data into a generative model and converting it into a different speech;

[1446] means for transmitting the converted audio data to a user's terminal;

[1447] means for playing the received converted voice data;

[1448] A system including:

[1449] (Claim 2)

[1450] a pre-processing means for performing noise reduction and volume normalization on the recorded voice data;

[1451] means for transforming the speech based on the speech profile selected by the generative model;

[1452] 10. The system of claim 1, comprising:

[1453] (Claim 3)

[1454] a means for selecting the audio profile from a plurality of types in advance;

[1455] 10. The system of claim 1, wherein the communication protocol and storage includes means for ensuring data security using encryption techniques.

[1456] "Example 1"

[1457] (Claim 1)

[1458] a means for recording the user's voice;

[1459] means for transmitting the recorded voice data to a server;

[1460] A means for preprocessing the speech data on the server and inputting the speech into a generative model to convert it into a different speech;

[1461] means for transmitting the converted audio data to a user's terminal;

[1462] means for playing the received converted voice data;

[1463] A system including:

[1464] (Claim 2)

[1465] a pre-processing means for performing noise reduction and volume normalization on the recorded voice data;

[1466] means for transforming the speech based on the speech profile selected by the generative model;

[1467] 10. The system of claim 1, comprising:

[1468] (Claim 3)

[1469] a means for selecting the audio profile from a plurality of types in advance;

[1470] Includes means for communication protocols and storage to ensure data security using encryption technology

[1471] 10. The system of claim 1.

[1472] "Application Example 1"

[1473] (Claim 1)

[1474] a means for recording the user's voice;

[1475] means for transmitting the recorded voice data to a server;

[1476] A means for inputting the preprocessed speech data into a generative model and converting it into a different speech;

[1477] means for transmitting the converted audio data to a user's terminal;

[1478] means for playing the received converted voice data;

[1479] a means for converting voice into a specific voice profile in the robot and providing guidance and instructions in an industrial facility in multiple languages ​​or in the voice of a specific character;

[1480] means for displaying a processing status while the recorded audio data is being converted;

[1481] A system including:

[1482] (Claim 2)

[1483] a pre-processing means for performing noise reduction and volume normalization on the recorded voice data;

[1484] means for transforming the speech based on the speech profile selected by the generative model;

[1485] A means for the robot to apply a specific voice profile to provide guidance in multiple languages ​​or with different character voices;

[1486] 10. The system of claim 1, comprising:

[1487] (Claim 3)

[1488] a means for selecting the audio profile from a plurality of types in advance;

[1489] 10. The system of claim 1, wherein the communication protocol and storage includes means for ensuring data security using encryption techniques.

[1490] 2. The system according to claim 1, further comprising a means for performing the voice conversion process in real time when the robot provides guidance within an industrial facility or multilingual support.

[1491] "Example 2: Combining Emotion Engines"

[1492] (Claim 1)

[1493] a means for recording the user's voice;

[1494] means for transmitting the recorded voice data to a server;

[1495] A means of inputting preprocessed speech data into a generative AI model to convert it into a different speech;

[1496] means for transmitting the converted voice data to a user terminal;

[1497] means for playing the received converted voice data;

[1498] means for analyzing a user's emotional state and automatically selecting or correcting an audio profile based on the analysis;

[1499] A system including:

[1500] (Claim 2)

[1501] a pre-processing means for performing noise reduction and volume normalization on the recorded voice data;

[1502] means for the generative AI model to transform the voice based on the selected voice profile;

[1503] 10. The system of claim 1, comprising:

[1504] (Claim 3)

[1505] a means for selecting the audio profile from a plurality of types in advance;

[1506] 10. The system of claim 1, wherein the communication protocol and storage device include means for ensuring data security using encryption techniques.

[1507] "Application example 2 when combining emotion engines"

[1508] (Claim 1)

[1509] a means for recording the user's voice;

[1510] means for transmitting the recorded voice data to a server;

[1511] A means for inputting the preprocessed speech data into a generative model and converting it into a different speech;

[1512] means for recognizing a user's emotional state using an emotion engine and selecting or amending a voice profile based thereon;

[1513] means for transmitting the converted audio data to a user's terminal;

[1514] means for playing the received converted voice data;

[1515] A system including:

[1516] (Claim 2)

[1517] a pre-processing means for performing noise reduction and volume normalization on the recorded voice data;

[1518] means for transforming the speech based on the speech profile selected by the generative model;

[1519] a means by which the emotion engine analyzes the user's emotional state;

[1520] 10. The system of claim 1, comprising:

[1521] (Claim 3)

[1522] a means for selecting the audio profile from a plurality of types in advance;

[1523] 10. The system of claim 1, wherein the communication protocol and storage includes means for ensuring data security using encryption techniques. [Explanation of symbols]

[1524] 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. a means for recording the user's voice; means for transmitting the recorded voice data to a server; A means for inputting the preprocessed speech data into a generative model and converting it into a different speech; means for transmitting the converted audio data to a user's terminal; means for playing the received converted voice data; A system including:

2. a pre-processing means for performing noise reduction and volume normalization on the recorded voice data; means for transforming the speech based on the speech profile selected by the generative model; The system of claim 1 , comprising:

3. a means for selecting the audio profile from a plurality of types in advance; 10. The system of claim 1, wherein the communication protocol and storage includes means for ensuring data security using encryption techniques.

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