System

Abstract

An object of the system according to the embodiment is to accurately convert speech into text and further convert the text into natural speech.SOLUTION: A system according to an embodiment includes a voice acquisition unit, a text generation unit, a text editing unit, and a voice generation unit. The voice acquisition unit acquires voice. The text generation unit converts the voice acquired by the voice acquisition unit into text. The text editing unit edits the text generated by the text generation unit. The voice generation unit converts the text edited by the text editing unit into voice.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] Conventional technologies have had the problem of difficulty in accurately converting speech into text and then converting that text into natural-sounding speech.

[0005] The system according to the embodiment aims to accurately convert speech into text and then convert the text into natural-sounding speech. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech acquisition unit, a text generation unit, a text editing unit, and a speech generation unit. The speech acquisition unit acquires speech. The text generation unit converts the speech acquired by the speech acquisition unit into text. The text editing unit edits the text generated by the text generation unit. The speech generation unit converts the text edited by the text editing unit into speech. [Effects of the Invention]

[0007] The system according to the embodiment can accurately convert speech into text and then convert the text into natural-sounding speech. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A platform according to an embodiment of the present invention is a new platform that combines a speech-to-text service and a text-to-speech service. This platform utilizes the OpenAI Whisper API and Text-to-Speech (TTS) API to enable users to convert speech to text, edit it, and then convert the text back to speech for playback. This allows the platform to efficiently and accurately perform bidirectional speech-to-text conversion. For example, converting a meeting recording to text, editing the text, and converting it back to speech makes it easier to organize and share information. Additionally, listening to emails and articles reduces visual clutter and allows users to efficiently acquire information.

[0029] The platform according to the embodiment includes a speech capture unit, a text generation unit, a text editing unit, and a speech generation unit. The speech capture unit captures speech. For example, the speech capture unit captures real-time speech using a microphone. The speech capture unit can also upload and acquire audio files. The speech capture unit can also acquire streaming audio. For example, the speech capture unit captures conference audio in real time and transmits it to the text generation unit. The text generation unit converts the speech captured by the speech capture unit into text. For example, the text generation unit analyzes speech and generates text using the OpenAI Whisper API. The text generation unit can also convert speech to text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The text generation unit can also convert speech to text using speech recognition technology. For example, the text generation unit analyzes speech data and outputs the content of the speech as text. The text editing unit edits the text generated by the text generation unit. For example, the text editing unit corrects and edits the generated text. The text editing unit can also edit text based on grammar checks and style guidelines. The text editing unit can also add or delete content from the text. For example, the text editing unit converts the generated text into a format that is easy for users to edit. The voice generation unit converts the text edited by the text editing unit into voice. For example, the voice generation unit converts text into voice using a Text-to-Speech (TTS) API. The voice generation unit can also convert text into voice using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice generation unit can also convert text into voice using speech synthesis technology. For example, the voice generation unit analyzes text data and generates natural-sounding voice. As a result, the platform according to the embodiment can convert voice into text, edit it, and then convert it back into voice, allowing users to efficiently organize and share information.

[0030] The text generation unit adds a speaker identification function to the audio data, allowing it to convert speeches of multiple people into text individually even when multiple people are speaking. The text generation unit, for example, uses the speaker identification function to convert speeches of multiple people into text individually even when multiple people are speaking. For example, it analyzes a recording of a meeting and distinguishes and converts each speaker's utterances into text. The text generation unit also enhances the speaker identification function and displays speeches of each speaker in the audio data in different colors. For example, it displays speeches of different speakers in different colors, allowing a user to identify speakers at a glance. The text generation unit also uses the speaker identification function to preferentially convert speeches of specific speakers in the audio data into text. For example, it preferentially converts speeches of the meeting chairperson or important speakers into text to avoid missing important information. As a result, even when multiple people are speaking, the information of each speaker can be accurately understood by converting speeches individually.

[0031] The text generation unit can analyze background sounds in the audio data and highlight portions that contain specific keywords. The text generation unit, for example, analyzes background sounds in the audio data and highlights portions that contain specific keywords. For example, it analyzes a recording of a meeting and displays portions that contain important keywords in different colors. The text generation unit also uses background sound analysis technology to automatically extract portions of the audio data that contain specific keywords. For example, it analyzes a recording of a meeting and automatically converts portions that contain important keywords into text. The text generation unit also enhances background sound analysis technology to highlight portions of the audio data that contain specific keywords in real time. For example, it analyzes a recording of a meeting in real time and instantly displays portions that contain important keywords. In this way, by highlighting portions that contain specific keywords, important information will not be missed.

[0032] The text generation unit can convert audio data into text in real time and display it as live subtitles. The text generation unit adds, for example, a function to convert audio data into text in real time and display it as live subtitles. For example, the text generation unit analyzes the audio of a meeting or lecture in real time and displays it as subtitles. The text generation unit also enhances the real-time text generation function and highlights important parts of the audio data. For example, it analyzes a recording of a meeting in real time and displays important remarks in different colors. The text generation unit also uses the real-time text generation function to automatically highlight parts of the audio data that contain specific keywords. For example, it analyzes a recording of a meeting in real time and instantly displays parts that contain important keywords. This allows audio data to be converted into text in real time and displayed as live subtitles, allowing information to be grasped in real time.

[0033] The text generation unit can automatically translate audio data into different languages, making it usable at international conferences and lectures. The text generation unit, for example, automatically translates audio data into different languages, making it usable at international conferences and lectures. For example, it analyzes a recording of a meeting and translates it into multiple languages ​​to create text. The text generation unit also enhances the automatic translation function and prioritizes translating parts of the audio data that contain specific keywords. For example, it analyzes a recording of a meeting and prioritizes translating parts of the audio data that contain important keywords to create text. The text generation unit also uses the automatic translation function to highlight parts of the audio data that have been translated into a specific language. For example, it analyzes a recording of a meeting and displays parts that have been translated into a specific language in a different color. In this way, automatic translation of audio data into different languages ​​makes it usable at international conferences and lectures.

[0034] The speech generation unit can analyze the context of the text data and insert appropriate intonation and pauses. The speech generation unit, for example, analyzes the context of the text data and inserts appropriate intonation and pauses. For example, the speech generation unit adjusts intonation according to punctuation and conjunctions at the end of sentences to generate natural speech. The speech generation unit also uses context analysis to highlight important parts of the text data. For example, important keywords and phrases can be highlighted to allow the user to grasp important information at a glance. The speech generation unit also uses context analysis to apply a speech tone that corresponds to a specific context in the text data. For example, an interrogative tone can be applied to questions, and an emphasis tone can be applied to imperative sentences. This allows natural speech reproduction by inserting intonation and pauses according to the context.

[0035] The voice generation unit can dynamically change the volume and speed of the voice to emphasize important portions of the text data. The voice generation unit, for example, dynamically changes the volume and speed of the voice to emphasize important portions of the text data. For example, the voice volume is increased to emphasize important keywords or phrases. The voice generation unit also dynamically changes the speed of the voice to emphasize important portions of the text data. For example, the voice speed is slowed down in portions that convey important information to make it easier for the user to understand. The voice generation unit also dynamically changes the volume and speed of the voice to emphasize portions of the text data with large emotional fluctuations. For example, the voice volume is increased in portions with large emotional fluctuations to emphasize the emotions. In this way, important information can be emphasized by dynamically changing the volume and speed of the voice to emphasize important portions.

[0036] The speech generation unit can automatically translate text data into different languages ​​and convert the translated text into speech. The speech generation unit adds, for example, a function for automatically translating text data into different languages ​​and converting the translated text into speech. For example, the speech generation unit translates English text into Japanese and generates Japanese speech. The speech generation unit also enhances the automatic translation function and prioritizes translating portions of the text data that contain specific keywords. For example, portions that contain important keywords are prioritized for translation and speech generation. The speech generation unit also uses the automatic translation function to highlight portions of the text data that have been translated into a specific language. For example, portions that have been translated into a specific language are displayed in different colors, allowing the user to understand the translation results at a glance. This enables international use by automatically translating text data into different languages ​​and converting the translated text into speech.

[0037] The audio generation unit can automatically add background music and sound effects when converting text data into audio. The audio generation unit, for example, provides a function for automatically adding background music and sound effects when converting text data into audio. For example, when converting a news article into audio, background music that matches the theme of the news is added. The audio generation unit also enhances the function for automatically adding background music and sound effects, and adds sound effects to portions of the text data that contain specific keywords. For example, sound effects are added to portions that contain important keywords to attract the user's attention. The audio generation unit also uses the function for automatically adding background music and sound effects to emphasize portions of the text data that show large emotional fluctuations. For example, background music is added to portions that show large emotional fluctuations to emphasize the emotions. In this way, by automatically adding background music and sound effects when converting into audio, more attractive audio content can be provided.

[0038] The text editing unit can perform grammar checks and make correction suggestions based on style guidelines for the text being edited. For example, the text editing unit performs grammar checks on the text being edited and makes suggestions to correct grammatical errors. For example, the text editing unit automatically detects grammatical errors and presents suggested corrections. The text editing unit also makes suggestions to unify the style of the text being edited based on style guidelines. For example, it presents suggested corrections to unify writing style and expression. The text editing unit also combines grammar checks and style guidelines to make suggestions to improve the quality of the text being edited. For example, it corrects grammatical errors while revising the expression to conform to the style guidelines. In this way, the quality of the text is improved by making suggested corrections based on grammar checks and style guidelines.

[0039] The text editing unit can automatically refer to relevant external information (e.g., dictionaries and technical glossaries) for the text being edited and suggest appropriate corrections. The text editing unit, for example, automatically refers to relevant dictionaries and technical glossaries for the text being edited and suggest appropriate corrections. For example, it may suggest the correct usage of technical terms. The text editing unit also refers to external information to suggest correcting incorrect information in the text being edited. For example, it may correct incorrect data or facts with correct information. The text editing unit also uses dictionaries and technical glossaries to suggest clarifying ambiguous expressions in the text being edited. For example, it may suggest replacing ambiguous expressions with more specific terms. In this way, the accuracy of the text is improved by automatically referring to relevant external information and suggesting appropriate corrections.

[0040] The text editing unit can add a function that allows text being edited to be shared with other users in real time and enables collaborative editing. The text editing unit adds a function that allows text being edited to be shared with other users in real time and enables collaborative editing. For example, it allows multiple users to edit text simultaneously. The text editing unit also enhances the collaborative editing function and displays the editing history in real time. For example, the edits made by each user can be displayed in different colors, making it possible to see at a glance who edited which part. The text editing unit also uses the collaborative editing function to collect feedback on the text being edited in real time. For example, comments and suggested corrections from other users can be displayed in real time. This allows text being edited to be shared with other users in real time and enables collaborative editing, making editing work more efficient.

[0041] The text editing unit can automatically convert text being edited into a different format (e.g., PDF or Word). The text editing unit provides, for example, a function for automatically converting text being edited into a different format. For example, the text is converted into PDF or Word format and saved. The text editing unit also enhances the automatic conversion function to convert text while retaining its format. For example, fonts and layout are retained when converted into PDF or Word format. The text editing unit also uses the function for automatic conversion into different formats to save the text being edited in multiple formats. For example, the text is saved in both PDF and Word format, allowing the user to select as needed. This makes it possible to save the text being edited in a variety of formats by automatically converting it into a different format.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The audio capture unit can add a noise removal function to the audio data. For example, it can analyze a recording of a meeting and automatically remove background noise. The audio capture unit can also enhance the noise removal function and filter out noise in a specific frequency band. For example, it can remove sounds from an air conditioner or projector to obtain clear audio data. The audio capture unit can also use the noise removal function to emphasize important parts of the audio data. For example, by emphasizing the speaker's voice and reducing background noise, important information can be made clearer. In this way, adding the noise removal function can improve the quality of the audio data.

[0044] The text generation unit adds a speaker identification function to the audio data, allowing it to convert each voice into text individually even when multiple people are speaking. For example, by using the speaker identification function for audio data, it can convert each voice into text individually even when multiple people are speaking. For example, it can analyze a recording of a meeting and distinguish and convert each speaker's utterances into text. The text generation unit also enhances the speaker identification function and displays each speaker's utterance in the audio data in a different color. For example, it can display the utterances of different speakers in different colors, allowing the user to identify the speaker at a glance. The text generation unit also uses the speaker identification function to prioritize the conversion of utterances by specific speakers in the audio data into text. For example, it can prioritize the conversion of utterances by the chairperson of a meeting or an important speaker to ensure that important information is not missed. As a result, even when multiple people are speaking, it can convert each speaker's utterance into text individually, allowing the information of each speaker to be accurately understood.

[0045] The text generation unit can analyze background sounds in the audio data and highlight portions that contain specific keywords. For example, the text generation unit analyzes background sounds in the audio data and highlights portions that contain specific keywords. For example, the text generation unit analyzes a recording of a meeting and displays portions that contain important keywords in different colors. The text generation unit also uses background sound analysis technology to automatically extract portions of the audio data that contain specific keywords. For example, the text generation unit analyzes a recording of a meeting and automatically converts portions that contain important keywords into text. The text generation unit also enhances background sound analysis technology to highlight portions of the audio data that contain specific keywords in real time. For example, the text generation unit analyzes a recording of a meeting in real time and instantly displays portions that contain important keywords. By highlighting portions that contain specific keywords, important information is not missed.

[0046] The text generation unit can convert audio data into text in real time and display it as live subtitles. For example, a function is added to convert audio data into text in real time and display it as live subtitles. For example, the audio of a meeting or lecture can be analyzed in real time and displayed as subtitles. The text generation unit also enhances the real-time text generation function to highlight important parts of the audio data. For example, it can analyze a recording of a meeting in real time and display important remarks in different colors. The text generation unit also uses the real-time text generation function to automatically highlight parts of the audio data that contain specific keywords. For example, it can analyze a recording of a meeting in real time and instantly display parts that contain important keywords. This allows audio data to be converted into text in real time and displayed as live subtitles, allowing information to be grasped in real time.

[0047] The text generation unit can automatically translate audio data into different languages, making it usable at international conferences and lectures. For example, audio data can be automatically translated into different languages, making it usable at international conferences and lectures. For example, a recording of a meeting can be analyzed, translated into multiple languages, and converted into text. The text generation unit also enhances the automatic translation function and prioritizes translating parts of the audio data that contain specific keywords. For example, a recording of a meeting can be analyzed, and parts that contain important keywords can be prioritized and translated into text. The text generation unit also uses the automatic translation function to highlight parts of the audio data that have been translated into a specific language. For example, a recording of a meeting can be analyzed, and parts that have been translated into a specific language can be displayed in a different color. This allows audio data to be automatically translated into different languages, making it usable at international conferences and lectures.

[0048] The speech generation unit can analyze the context of the text data and insert appropriate intonation and pauses. For example, it analyzes the context of the text data and inserts appropriate intonation and pauses. For example, it adjusts intonation according to punctuation and conjunctions at the end of sentences to generate natural speech. The speech generation unit also uses context analysis to highlight important parts of the text data. For example, it highlights important keywords and phrases so that the user can grasp important information at a glance. The speech generation unit also uses context analysis to apply a speech tone that corresponds to a specific context in the text data. For example, it applies an interrogative tone to questions and an emphasis tone to commands. This allows natural speech reproduction by inserting intonation and pauses according to the context.

[0049] The speech generation unit can dynamically change the volume and speed of the speech to emphasize important portions of the text data. For example, the speech volume and speed are dynamically changed to emphasize important portions of the text data. For example, the speech volume is increased to emphasize important keywords or phrases. The speech generation unit also dynamically changes the speed of the speech to emphasize important portions of the text data. For example, the speech speed is slowed down in portions that convey important information to make it easier for the user to understand. The speech generation unit also dynamically changes the volume and speed of the speech to emphasize portions of the text data with large emotional fluctuations. For example, the speech volume is increased in portions with large emotional fluctuations to emphasize the emotions. In this way, important information can be emphasized by dynamically changing the volume and speed of the speech to emphasize important portions.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The audio capture unit captures audio. For example, the audio capture unit captures real-time audio using a microphone. The audio capture unit can also upload and capture audio files. The audio capture unit can also capture streaming audio. For example, the audio capture unit captures conference audio in real time and transmits it to the text generation unit. Step 2: The text generation unit converts the speech captured by the speech capture unit into text. For example, the text generation unit analyzes speech using the OpenAI Whisper API and generates text. The text generation unit can also convert speech into text using a generation AI (for example, a text generation AI or a multimodal generation AI). The text generation unit can also convert speech into text using speech recognition technology. For example, the text generation unit analyzes speech data and outputs the content of the speech as text. Step 3: The text editing unit edits the text generated by the text generation unit. For example, the text editing unit corrects and edits the generated text. The text editing unit can also edit the text based on grammar checks and style guidelines. The text editing unit can also add or delete content from the text. For example, the text editing unit converts the generated text into a format that is easy for users to edit. Step 4: The speech generation unit converts the text edited by the text editing unit into speech. For example, the speech generation unit converts text into speech using a Text-to-Speech (TTS) API. The speech generation unit can also convert text into speech using a generation AI (e.g., a text generation AI or a multimodal generation AI). The speech generation unit can also convert text into speech using speech synthesis technology. For example, the speech generation unit analyzes text data and generates natural-sounding speech.

[0052] (Example 2) A platform according to an embodiment of the present invention is a new platform that combines a speech-to-text service and a text-to-speech service. This platform utilizes the OpenAI Whisper API and Text-to-Speech (TTS) API to enable users to convert speech to text, edit it, and then convert the text back to speech for playback. This allows the platform to efficiently and accurately perform bidirectional speech-to-text conversion. For example, converting a meeting recording to text, editing the text, and converting it back to speech makes it easier to organize and share information. Additionally, listening to emails and articles reduces visual clutter and allows users to efficiently acquire information.

[0053] The platform according to the embodiment includes a speech capture unit, a text generation unit, a text editing unit, and a speech generation unit. The speech capture unit captures speech. For example, the speech capture unit captures real-time speech using a microphone. The speech capture unit can also upload and acquire audio files. The speech capture unit can also acquire streaming audio. For example, the speech capture unit captures conference audio in real time and transmits it to the text generation unit. The text generation unit converts the speech captured by the speech capture unit into text. For example, the text generation unit analyzes speech and generates text using the OpenAI Whisper API. The text generation unit can also convert speech to text using a generation AI (e.g., a text generation AI or a multimodal generation AI). The text generation unit can also convert speech to text using speech recognition technology. For example, the text generation unit analyzes speech data and outputs the content of the speech as text. The text editing unit edits the text generated by the text generation unit. For example, the text editing unit corrects and edits the generated text. The text editing unit can also edit text based on grammar checks and style guidelines. The text editing unit can also add or delete content from the text. For example, the text editing unit converts the generated text into a format that is easy for users to edit. The voice generation unit converts the text edited by the text editing unit into voice. For example, the voice generation unit converts text into voice using a Text-to-Speech (TTS) API. The voice generation unit can also convert text into voice using a generation AI (e.g., a text generation AI or a multimodal generation AI). The voice generation unit can also convert text into voice using speech synthesis technology. For example, the voice generation unit analyzes text data and generates natural-sounding voice. As a result, the platform according to the embodiment can convert voice into text, edit it, and then convert it back into voice, allowing users to efficiently organize and share information.

[0054] The text generation unit can perform emotion analysis of the voice data and determine the priority of text generation based on the intensity and type of emotion. The text generation unit, for example, performs emotion analysis of the voice data and quantifies the intensity and type of emotion. For example, emotions such as joy, sadness, and anger are scored, and portions with high emotion scores are preferentially converted to text. The text generation unit also analyzes voice data from meetings and lectures and identifies important statements based on the intensity and type of emotion. For example, emotionally charged scenes are preferentially converted to text so as not to miss important discussions. The text generation unit also uses emotion analysis to highlight portions of the voice data with large emotional fluctuations. For example, portions with large emotional fluctuations are displayed in different colors, allowing the user to grasp important portions at a glance. In this way, by determining the priority of text generation based on the intensity and type of emotion, important information is not missed.

[0055] The text generation unit adds a speaker identification function to the audio data, allowing it to convert speeches of multiple people into text individually even when multiple people are speaking. The text generation unit, for example, uses the speaker identification function to convert speeches of multiple people into text individually even when multiple people are speaking. For example, it analyzes a recording of a meeting and distinguishes and converts each speaker's utterances into text. The text generation unit also enhances the speaker identification function and displays speeches of each speaker in the audio data in different colors. For example, it displays speeches of different speakers in different colors, allowing a user to identify speakers at a glance. The text generation unit also uses the speaker identification function to preferentially convert speeches of specific speakers in the audio data into text. For example, it preferentially converts speeches of the meeting chairperson or important speakers into text to avoid missing important information. As a result, even when multiple people are speaking, the information of each speaker can be accurately understood by converting speeches individually.

[0056] The text generation unit can analyze background sounds in the audio data and highlight portions that contain specific keywords. The text generation unit, for example, analyzes background sounds in the audio data and highlights portions that contain specific keywords. For example, it analyzes a recording of a meeting and displays portions that contain important keywords in different colors. The text generation unit also uses background sound analysis technology to automatically extract portions of the audio data that contain specific keywords. For example, it analyzes a recording of a meeting and automatically converts portions that contain important keywords into text. The text generation unit also enhances background sound analysis technology to highlight portions of the audio data that contain specific keywords in real time. For example, it analyzes a recording of a meeting in real time and instantly displays portions that contain important keywords. In this way, by highlighting portions that contain specific keywords, important information will not be missed.

[0057] The text generation unit can convert audio data into text in real time and display it as live subtitles. The text generation unit adds, for example, a function to convert audio data into text in real time and display it as live subtitles. For example, the text generation unit analyzes the audio of a meeting or lecture in real time and displays it as subtitles. The text generation unit also enhances the real-time text generation function and highlights important parts of the audio data. For example, it analyzes a recording of a meeting in real time and displays important remarks in different colors. The text generation unit also uses the real-time text generation function to automatically highlight parts of the audio data that contain specific keywords. For example, it analyzes a recording of a meeting in real time and instantly displays parts that contain important keywords. This allows audio data to be converted into text in real time and displayed as live subtitles, allowing information to be grasped in real time.

[0058] The text generation unit can automatically translate audio data into different languages, making it usable at international conferences and lectures. The text generation unit, for example, automatically translates audio data into different languages, making it usable at international conferences and lectures. For example, it analyzes a recording of a meeting and translates it into multiple languages ​​to create text. The text generation unit also enhances the automatic translation function and prioritizes translating parts of the audio data that contain specific keywords. For example, it analyzes a recording of a meeting and prioritizes translating parts of the audio data that contain important keywords to create text. The text generation unit also uses the automatic translation function to highlight parts of the audio data that have been translated into a specific language. For example, it analyzes a recording of a meeting and displays parts that have been translated into a specific language in a different color. In this way, automatic translation of audio data into different languages ​​makes it usable at international conferences and lectures.

[0059] The text generation unit can use the emotion estimation function to estimate the emotion of the user when inputting voice in real time and make suggestions to elicit positive emotions. The text generation unit, for example, uses the emotion estimation function to estimate the emotion of the user when inputting voice in real time. For example, the text generation unit analyzes the tone and speed of the user's voice and calculates an emotion score. The text generation unit also enhances the emotion estimation function to make suggestions to elicit positive emotions when the user inputs voice. For example, if the user's emotion score is low, an encouraging message is displayed. The text generation unit also uses the emotion estimation function to monitor the emotion of the user when inputting voice in real time and provide advice to elicit positive emotions. For example, if the user's emotion score is low, a suggestion to relax is made. In this way, the user's experience is improved by estimating the emotion of the user when inputting voice in real time and making suggestions to elicit positive emotions.

[0060] The voice generation unit can perform emotion analysis of the text data and automatically adjust the voice tone according to the emotion. The voice generation unit, for example, performs emotion analysis of the text data and automatically adjusts the voice tone according to the emotion. For example, a bright tone is applied to text with a strong emotion of joy, and a calm tone is applied to text with a strong emotion of sadness. The voice generation unit also uses emotion analysis to highlight parts of the text data with large emotional fluctuations. For example, parts with large emotional fluctuations are displayed in different colors so that the user can grasp changes in emotion at a glance. The voice generation unit also uses emotion analysis to preferentially vocalize parts of the text data with a particular strong emotion. For example, parts with a high emotion score are preferentially vocalized so as not to miss important information. In this way, by automatically adjusting the voice tone according to the emotion, natural voice reproduction is possible.

[0061] The speech generation unit can analyze the context of the text data and insert appropriate intonation and pauses. The speech generation unit, for example, analyzes the context of the text data and inserts appropriate intonation and pauses. For example, the speech generation unit adjusts intonation according to punctuation and conjunctions at the end of sentences to generate natural speech. The speech generation unit also uses context analysis to highlight important parts of the text data. For example, important keywords and phrases can be highlighted to allow the user to grasp important information at a glance. The speech generation unit also uses context analysis to apply a speech tone that corresponds to a specific context in the text data. For example, an interrogative tone can be applied to questions, and an emphasis tone can be applied to imperative sentences. This allows natural speech reproduction by inserting intonation and pauses according to the context.

[0062] The voice generation unit can dynamically change the volume and speed of the voice to emphasize important portions of the text data. The voice generation unit, for example, dynamically changes the volume and speed of the voice to emphasize important portions of the text data. For example, the voice volume is increased to emphasize important keywords or phrases. The voice generation unit also dynamically changes the speed of the voice to emphasize important portions of the text data. For example, the voice speed is slowed down in portions that convey important information to make it easier for the user to understand. The voice generation unit also dynamically changes the volume and speed of the voice to emphasize portions of the text data with large emotional fluctuations. For example, the voice volume is increased in portions with large emotional fluctuations to emphasize the emotions. In this way, important information can be emphasized by dynamically changing the volume and speed of the voice to emphasize important portions.

[0063] The speech generation unit can automatically translate text data into different languages ​​and convert the translated text into speech. The speech generation unit adds, for example, a function for automatically translating text data into different languages ​​and converting the translated text into speech. For example, the speech generation unit translates English text into Japanese and generates Japanese speech. The speech generation unit also enhances the automatic translation function and prioritizes translating portions of the text data that contain specific keywords. For example, portions that contain important keywords are prioritized for translation and speech generation. The speech generation unit also uses the automatic translation function to highlight portions of the text data that have been translated into a specific language. For example, portions that have been translated into a specific language are displayed in different colors, allowing the user to understand the translation results at a glance. This enables international use by automatically translating text data into different languages ​​and converting the translated text into speech.

[0064] The audio generation unit can automatically add background music and sound effects when converting text data into audio. The audio generation unit, for example, provides a function for automatically adding background music and sound effects when converting text data into audio. For example, when converting a news article into audio, background music that matches the theme of the news is added. The audio generation unit also enhances the function for automatically adding background music and sound effects, and adds sound effects to portions of the text data that contain specific keywords. For example, sound effects are added to portions that contain important keywords to attract the user's attention. The audio generation unit also uses the function for automatically adding background music and sound effects to emphasize portions of the text data that show large emotional fluctuations. For example, background music is added to portions that show large emotional fluctuations to emphasize the emotions. In this way, by automatically adding background music and sound effects when converting into audio, more attractive audio content can be provided.

[0065] The voice generation unit can use the emotion estimation function to estimate the emotion of the user when entering text in real time and make suggestions to elicit positive emotions. The voice generation unit, for example, uses the emotion estimation function to estimate the emotion of the user when entering text in real time. For example, the emotion estimation function may be used to analyze the user's input speed and writing style and calculate an emotion score. The voice generation unit also enhances the emotion estimation function to make suggestions to elicit positive emotions when the user enters text. For example, if the user's emotion score is low, an encouraging message may be displayed. The voice generation unit also uses the emotion estimation function to monitor the emotion of the user when entering text in real time and provide advice to elicit positive emotions. For example, if the user's emotion score is low, a suggestion to relax may be made. In this way, the user's experience is improved by estimating the emotion of the user when entering text in real time and making suggestions to elicit positive emotions.

[0066] The text editing unit can perform a sentiment analysis on the text being edited and automatically generate a correction suggestion based on the sentiment. The text editing unit, for example, performs a sentiment analysis on the text being edited and automatically generates a correction suggestion based on the sentiment. For example, the text editing unit makes a suggestion to correct a part with a strong negative sentiment to a more positive expression. The text editing unit also uses the sentiment analysis to highlight parts of the text being edited that have large emotional fluctuations. For example, the parts with large emotional fluctuations are displayed in different colors so that the user can grasp the changes in sentiment at a glance. The text editing unit also uses the sentiment analysis to make a suggestion to preferentially correct parts of the text being edited that have a particular strong emotion. For example, parts with a high sentiment score are preferentially corrected so as not to miss important information. In this way, the quality of the text is improved by automatically generating correction suggestions based on sentiment.

[0067] The text editing unit can perform grammar checks and make correction suggestions based on style guidelines for the text being edited. For example, the text editing unit performs grammar checks on the text being edited and makes suggestions to correct grammatical errors. For example, the text editing unit automatically detects grammatical errors and presents suggested corrections. The text editing unit also makes suggestions to unify the style of the text being edited based on style guidelines. For example, it presents suggested corrections to unify writing style and expression. The text editing unit also combines grammar checks and style guidelines to make suggestions to improve the quality of the text being edited. For example, it corrects grammatical errors while revising the expression to conform to the style guidelines. In this way, the quality of the text is improved by making suggested corrections based on grammar checks and style guidelines.

[0068] The text editing unit can automatically refer to relevant external information (e.g., dictionaries and technical glossaries) for the text being edited and suggest appropriate corrections. The text editing unit, for example, automatically refers to relevant dictionaries and technical glossaries for the text being edited and suggest appropriate corrections. For example, it may suggest the correct usage of technical terms. The text editing unit also refers to external information to suggest correcting incorrect information in the text being edited. For example, it may correct incorrect data or facts with correct information. The text editing unit also uses dictionaries and technical glossaries to suggest clarifying ambiguous expressions in the text being edited. For example, it may suggest replacing ambiguous expressions with more specific terms. In this way, the accuracy of the text is improved by automatically referring to relevant external information and suggesting appropriate corrections.

[0069] The text editing unit can add a function that allows text being edited to be shared with other users in real time and enables collaborative editing. The text editing unit adds a function that allows text being edited to be shared with other users in real time and enables collaborative editing. For example, it allows multiple users to edit text simultaneously. The text editing unit also enhances the collaborative editing function and displays the editing history in real time. For example, the edits made by each user can be displayed in different colors, making it possible to see at a glance who edited which part. The text editing unit also uses the collaborative editing function to collect feedback on the text being edited in real time. For example, comments and suggested corrections from other users can be displayed in real time. This allows text being edited to be shared with other users in real time and enables collaborative editing, making editing work more efficient.

[0070] The text editing unit can automatically convert text being edited into a different format (e.g., PDF or Word). The text editing unit provides, for example, a function for automatically converting text being edited into a different format. For example, the text is converted into PDF or Word format and saved. The text editing unit also enhances the automatic conversion function to convert text while retaining its format. For example, fonts and layout are retained when converted into PDF or Word format. The text editing unit also uses the function for automatic conversion into different formats to save the text being edited in multiple formats. For example, the text is saved in both PDF and Word format, allowing the user to select as needed. This makes it possible to save the text being edited in a variety of formats by automatically converting it into a different format.

[0071] The text editing unit can use the emotion estimation function to estimate the emotion a user is feeling when editing text in real time and make suggestions to elicit positive emotions. The text editing unit, for example, uses the emotion estimation function to estimate the emotion a user is feeling when editing text in real time. For example, the text editing unit analyzes the user's editing speed and writing style and calculates an emotion score. The text editing unit also enhances the emotion estimation function to make suggestions to elicit positive emotions when the user is editing text. For example, if the user's emotion score is low, an encouraging message is displayed. The text editing unit also uses the emotion estimation function to monitor the emotion a user is feeling when editing text in real time and provide advice to elicit positive emotions. For example, if the user's emotion score is low, a suggestion to relax is made. In this way, the user's experience is improved by estimating the emotion a user is feeling when editing text in real time and making suggestions to elicit positive emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The audio capture unit can add a noise removal function to the audio data. For example, it can analyze a recording of a meeting and automatically remove background noise. The audio capture unit can also enhance the noise removal function and filter out noise in a specific frequency band. For example, it can remove sounds from an air conditioner or projector to obtain clear audio data. The audio capture unit can also use the noise removal function to emphasize important parts of the audio data. For example, by emphasizing the speaker's voice and reducing background noise, important information can be made clearer. In this way, adding the noise removal function can improve the quality of the audio data.

[0074] The text generation unit can perform emotion analysis of the voice data and determine the priority of text generation based on the intensity and type of emotion. For example, emotion analysis of the voice data is performed to quantify the intensity and type of emotion. For example, emotions such as joy, sadness, and anger are scored, and parts with high emotion scores are preferentially converted to text. The text generation unit also analyzes voice data from meetings and lectures and identifies important statements based on the intensity and type of emotion. For example, emotionally charged scenes are preferentially converted to text so as not to miss important discussions. The text generation unit also uses emotion analysis to highlight parts of the voice data with large emotional fluctuations. For example, parts with large emotional fluctuations are displayed in different colors, allowing the user to grasp important parts at a glance. In this way, by determining the priority of text generation based on the intensity and type of emotion, important information is not missed.

[0075] The text generation unit adds a speaker identification function to the audio data, allowing it to convert each voice into text individually even when multiple people are speaking. For example, by using the speaker identification function for audio data, it can convert each voice into text individually even when multiple people are speaking. For example, it can analyze a recording of a meeting and distinguish and convert each speaker's utterances into text. The text generation unit also enhances the speaker identification function and displays each speaker's utterance in the audio data in a different color. For example, it can display the utterances of different speakers in different colors, allowing the user to identify the speaker at a glance. The text generation unit also uses the speaker identification function to prioritize the conversion of utterances by specific speakers in the audio data into text. For example, it can prioritize the conversion of utterances by the chairperson of a meeting or an important speaker to ensure that important information is not missed. As a result, even when multiple people are speaking, it can convert each speaker's utterance into text individually, allowing the information of each speaker to be accurately understood.

[0076] The text generation unit can analyze background sounds in the audio data and highlight portions that contain specific keywords. For example, the text generation unit analyzes background sounds in the audio data and highlights portions that contain specific keywords. For example, the text generation unit analyzes a recording of a meeting and displays portions that contain important keywords in different colors. The text generation unit also uses background sound analysis technology to automatically extract portions of the audio data that contain specific keywords. For example, the text generation unit analyzes a recording of a meeting and automatically converts portions that contain important keywords into text. The text generation unit also enhances background sound analysis technology to highlight portions of the audio data that contain specific keywords in real time. For example, the text generation unit analyzes a recording of a meeting in real time and instantly displays portions that contain important keywords. By highlighting portions that contain specific keywords, important information is not missed.

[0077] The text generation unit can convert audio data into text in real time and display it as live subtitles. For example, a function is added to convert audio data into text in real time and display it as live subtitles. For example, the audio of a meeting or lecture can be analyzed in real time and displayed as subtitles. The text generation unit also enhances the real-time text generation function to highlight important parts of the audio data. For example, it can analyze a recording of a meeting in real time and display important remarks in different colors. The text generation unit also uses the real-time text generation function to automatically highlight parts of the audio data that contain specific keywords. For example, it can analyze a recording of a meeting in real time and instantly display parts that contain important keywords. This allows audio data to be converted into text in real time and displayed as live subtitles, allowing information to be grasped in real time.

[0078] The text generation unit can automatically translate audio data into different languages, making it usable at international conferences and lectures. For example, audio data can be automatically translated into different languages, making it usable at international conferences and lectures. For example, a recording of a meeting can be analyzed, translated into multiple languages, and converted into text. The text generation unit also enhances the automatic translation function and prioritizes translating parts of the audio data that contain specific keywords. For example, a recording of a meeting can be analyzed, and parts that contain important keywords can be prioritized and translated into text. The text generation unit also uses the automatic translation function to highlight parts of the audio data that have been translated into a specific language. For example, a recording of a meeting can be analyzed, and parts that have been translated into a specific language can be displayed in a different color. This allows audio data to be automatically translated into different languages, making it usable at international conferences and lectures.

[0079] The text generation unit can use the emotion estimation function to estimate the emotion of the user when inputting voice in real time and make suggestions to elicit positive emotions. For example, the emotion estimation function is used to estimate the emotion of the user when inputting voice in real time. For example, the emotion estimation function is used to analyze the tone and speed of the user's voice and calculate an emotion score. The text generation unit also enhances the emotion estimation function and makes suggestions to elicit positive emotions when the user inputs voice. For example, if the user's emotion score is low, an encouraging message is displayed. The text generation unit also uses the emotion estimation function to monitor the emotion of the user when inputting voice in real time and provide advice to elicit positive emotions. For example, if the user's emotion score is low, a suggestion to relax is made. In this way, the user's experience is improved by estimating the emotion of the user when inputting voice in real time and making suggestions to elicit positive emotions.

[0080] The speech generation unit can perform emotion analysis of the text data and automatically adjust the speech tone according to the emotion. For example, the speech generation unit performs emotion analysis of the text data and automatically adjusts the speech tone according to the emotion. For example, a bright tone is applied to text with a strong emotion of joy, and a calm tone is applied to text with a strong emotion of sadness. The speech generation unit also uses emotion analysis to highlight parts of the text data with large emotional fluctuations. For example, parts with large emotional fluctuations are displayed in different colors, allowing the user to grasp changes in emotion at a glance. The speech generation unit also uses emotion analysis to preferentially vocalize parts of the text data with a strong specific emotion. For example, parts with a high emotion score are preferentially vocalized to avoid missing important information. In this way, automatic adjustment of the speech tone according to the emotion enables natural speech reproduction.

[0081] The speech generation unit can analyze the context of the text data and insert appropriate intonation and pauses. For example, it analyzes the context of the text data and inserts appropriate intonation and pauses. For example, it adjusts intonation according to punctuation and conjunctions at the end of sentences to generate natural speech. The speech generation unit also uses context analysis to highlight important parts of the text data. For example, it highlights important keywords and phrases so that the user can grasp important information at a glance. The speech generation unit also uses context analysis to apply a speech tone that corresponds to a specific context in the text data. For example, it applies an interrogative tone to questions and an emphasis tone to commands. This allows natural speech reproduction by inserting intonation and pauses according to the context.

[0082] The speech generation unit can dynamically change the volume and speed of the speech to emphasize important portions of the text data. For example, the speech volume and speed are dynamically changed to emphasize important portions of the text data. For example, the speech volume is increased to emphasize important keywords or phrases. The speech generation unit also dynamically changes the speed of the speech to emphasize important portions of the text data. For example, the speech speed is slowed down in portions that convey important information to make it easier for the user to understand. The speech generation unit also dynamically changes the volume and speed of the speech to emphasize portions of the text data with large emotional fluctuations. For example, the speech volume is increased in portions with large emotional fluctuations to emphasize the emotions. In this way, important information can be emphasized by dynamically changing the volume and speed of the speech to emphasize important portions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The audio capture unit captures audio. For example, the audio capture unit captures real-time audio using a microphone. The audio capture unit can also upload and capture audio files. The audio capture unit can also capture streaming audio. For example, the audio capture unit captures conference audio in real time and transmits it to the text generation unit. Step 2: The text generation unit converts the speech captured by the speech capture unit into text. For example, the text generation unit analyzes speech using the OpenAI Whisper API and generates text. The text generation unit can also convert speech into text using a generation AI (for example, a text generation AI or a multimodal generation AI). The text generation unit can also convert speech into text using speech recognition technology. For example, the text generation unit analyzes speech data and outputs the content of the speech as text. Step 3: The text editing unit edits the text generated by the text generation unit. For example, the text editing unit corrects and edits the generated text. The text editing unit can also edit the text based on grammar checks and style guidelines. The text editing unit can also add or delete content from the text. For example, the text editing unit converts the generated text into a format that is easy for users to edit. Step 4: The speech generation unit converts the text edited by the text editing unit into speech. For example, the speech generation unit converts text into speech using a Text-to-Speech (TTS) API. The speech generation unit can also convert text into speech using a generation AI (e.g., a text generation AI or a multimodal generation AI). The speech generation unit can also convert text into speech using speech synthesis technology. For example, the speech generation unit analyzes text data and generates natural-sounding speech.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0126] 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.

[0127] 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.

[0128] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] 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.

[0135] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0136] 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.

[0137] 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).

[0138] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, 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.

[0139] 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."

[0140] 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.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0146] The hardware resource that executes the specific process 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 process may be a single processor.

[0147] 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.

[0148] 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.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice acquisition unit that acquires voice; a text generation unit that converts the speech acquired by the speech acquisition unit into text; a text editing unit that edits the text generated by the text generating unit; a voice generating unit that converts the text edited by the text editing unit into voice; A system characterized by:

2. The text generation unit Performs sentiment analysis of voice data and determines the priority of text conversion based on the intensity and type of emotion 2. The system of claim 1.

3. The text generation unit Added speaker identification function for voice data, converting voice data into text individually even when multiple people are speaking 2. The system of claim 1.

4. The text generation unit Analyzes background sounds in audio data and highlights parts containing specific keywords 2. The system of claim 1.

5. The text generation unit Converts audio data into text in real time and displays it as live subtitles 2. The system of claim 1.

Images