System

Abstract

An object of a system according to an embodiment is to allow people with limited hearing to smoothly perform communication in daily life.SOLUTION: A system according to an embodiment includes a voice acquisition unit, a text generation unit, a voice generation unit, and an answer generation unit. The voice acquisition unit acquires voice. The text generation unit converts the voice acquired by the voice acquisition unit into text. The speech generation unit converts the text generated by the text generation unit into speech. The response generation unit understands the content of the dialogue and generates an appropriate response.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 making it difficult for people with hearing impairments to communicate in their daily lives.

[0005] The system according to the embodiment aims to enable people with hearing limitations to communicate smoothly in their daily lives. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech acquisition unit, a text generation unit, a speech generation unit, and a response generation unit. The speech acquisition unit acquires speech. The text generation unit converts the speech acquired by the speech acquisition unit into text. The speech generation unit converts the text generated by the text generation unit into speech. The response generation unit understands the content of the dialogue and generates an appropriate response. [Effects of the Invention]

[0007] The system according to the embodiment enables people with hearing limitations to communicate smoothly in their daily lives. [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) The hearing aid system according to the embodiment of the present invention is a system that converts speech into text and vice versa, thereby enabling people with hearing limitations to communicate smoothly in their daily lives.

[0029] A hearing assistance system according to an embodiment includes a voice capture unit, a text generation unit, a speech generation unit, and a response generation unit. The voice capture unit captures voice. For example, the voice capture unit captures surrounding voice using a microphone. Alternatively, the voice capture unit may use a built-in microphone of a smartphone or tablet. Alternatively, the voice capture unit may capture voice using a Bluetooth microphone. For example, the voice capture unit captures voice during a conversation in real time and transmits the voice to the system. The text generation unit converts the voice captured by the voice capture unit into text. For example, the text generation unit analyzes voice data using OpenAI's Whisper API and generates corresponding text. Alternatively, the text generation unit may convert voice into text using speech recognition technology. Alternatively, the text generation unit may convert voice data into text in real time. For example, the text generation unit converts voice during a conversation into text in real time and displays it on a screen. The voice generation unit converts the text generated by the text generation unit into voice. For example, the voice generation unit converts text into voice using a Text-to-Speech (TTS) API. The speech generation unit can also convert text into speech using speech synthesis technology. The speech generation unit can also convert text data into speech in real time. For example, the speech generation unit converts text input by a user into speech in real time and outputs it from a speaker. The response generation unit understands the content of a dialogue and generates an appropriate response. For example, the response generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to understand the context of the dialogue and generate an appropriate response. The response generation unit can also generate a response based on a user's input. The response generation unit can also analyze the content of the dialogue and generate an appropriate response. For example, the response generation unit generates an appropriate response to a question input by a user and outputs it as speech via the speech generation unit. This enables people with hearing limitations to communicate smoothly in their daily lives. For example, people with hearing limitations can understand what others are saying in text and express their opinions through speech.It also allows people with hearing impairments to understand the content of conversations and generate appropriate responses, allowing them to enjoy social life more freely.

[0030] In the hearing assistance system, the speech capture unit automatically removes background noise from the speech data and implements an algorithm to enable more accurate text conversion. For example, the speech capture unit analyzes speech data in real time and implements an algorithm to automatically remove background noise. For example, noise filtering technology is used to achieve accurate text conversion even in noisy environments. The speech capture unit also applies noise reduction technology as preprocessing for the speech data and sends clear speech data to the Whisper API. For example, echo cancellation and noise gates are used to improve the quality of the speech. The speech capture unit also trains a machine learning model to remove background noise from the speech data and improves its ability to distinguish between noise and speech. For example, the model is trained using noise samples and then applied to actual speech data. This removes background noise, enabling more accurate text conversion.

[0031] In a hearing assistance system, a text generation unit identifies the speaker of an audio signal and applies a different text style to each speaker. The text generation unit, for example, employs an algorithm to identify speakers from audio data and applies a different text style to each speaker. For example, it analyzes the speaker's voice characteristics and generates an individual text format. The text generation unit also uses speaker identification technology to analyze a conversation involving multiple speakers and distinguish and convert each speaker's utterances into text. For example, when automatically generating meeting minutes, the utterances of each speaker are clearly distinguished. The text generation unit also extracts speaker characteristics from the audio data and applies individual style templates to apply a different text style to each speaker. For example, it distinguishes between formal and casual conversations. This allows for clear distinction between speakers even when there are multiple speakers by applying different text styles to each speaker.

[0032] In the hearing assistance system, the text generation unit applies speech-to-text conversion to the conversion of sign language and gesture movements into text. For example, the text generation unit captures sign language and gesture movements with a camera and implements an algorithm to convert those movements into text. For example, it analyzes sign language movements in real time and generates corresponding text. The text generation unit also trains a machine learning model to analyze sign language and gesture movements and uses that model to perform text conversion. For example, it collects sign language movements as a dataset and trains the model. The text generation unit also develops a dedicated device to convert sign language and gesture movements into text and links that device to the Whisper API. For example, it detects hand movements with a sensor and converts them into text. This makes it possible to convert sign language and gesture movements into text.

[0033] In a hearing assistance system, the text generation unit translates voice data in real time, enabling text conversion between different languages. The text generation unit, for example, introduces an algorithm that translates voice data in real time to achieve text conversion between different languages. For example, it converts English voice into Japanese text. The text generation unit also analyzes voice data and builds a system that translates it into different languages ​​in real time via a translation API. For example, it displays text in multiple languages ​​during a meeting. The text generation unit also trains a machine learning model for translating voice data in real time and links the model to the Whisper API. For example, it develops a system that performs voice recognition and translation simultaneously. This enables text conversion between different languages.

[0034] In a hearing assistance system, a speech generation unit automatically adjusts the tone and speed of speech according to the content of the text. For example, the speech generation unit implements an algorithm that analyzes the content of the text and automatically adjusts the tone and speed of speech according to that content. For example, the tone may be raised to emphasize important parts. The speech generation unit also builds a system that understands the context of the text and selects an appropriate tone and speed of speech. For example, an interrogative tone may be applied to questions. The speech generation unit also adds a function to adjust the tone and speed of speech in real time based on the content of the text. For example, an emotional tone may be applied to emotional content. This makes it possible to adjust the tone and speed of speech according to the content of the text.

[0035] The hearing assistance system incorporates an algorithm in which the speech generation unit understands the context of the text and generates speech with appropriate intonation and emotion. The speech generation unit incorporates, for example, an algorithm that analyzes the context of the text and generates speech with appropriate intonation and emotion. For example, emotionally rich speech is applied to emotional scenes. The speech generation unit also constructs a system that understands the content of the text and generates speech with appropriate intonation and emotion. For example, different emotions are expressed for each character in a story reading. The speech generation unit also adds a function that analyzes the context of the text and generates speech with appropriate intonation and emotion in real time. For example, the tone of the speech is adjusted according to changes in emotion. This makes it possible to understand the context of the text and generate speech with appropriate intonation and emotion.

[0036] In hearing assistance systems, the speech generation unit applies text-to-speech conversion to the conversion of visual information (e.g., images or videos) into speech. The speech generation unit, for example, introduces an algorithm that analyzes the content of images or videos and converts that content into speech. For example, it generates a description of the image and converts it into speech. The speech generation unit also builds a system that analyzes visual information, converts it into text, and then converts it into speech. For example, it generates subtitles for videos and converts them into speech. The speech generation unit also adds a function that analyzes the content of images or videos in real time and converts that content into speech. For example, it explains the content of live video in audio. This makes it possible to convert visual information into speech as well.

[0037] The hearing assistance system adds a function that allows the voice generation unit to select different character voices when converting text to speech, thereby enabling speech generation according to the user's preferences. For example, the speech generation unit adds a function that allows the voice generation unit to select different character voices when converting text to speech. For example, it makes it possible to select male voices, female voices, children's voices, etc. The speech generation unit also builds a system that allows the voice generation unit to select different character voices according to the user's preferences. For example, it makes it possible to select the voice of an anime character. The speech generation unit also adds a function that allows the voice generation unit to select different character voices in real time when converting text to speech. For example, it makes it possible for the user to listen to voice samples and make a selection. This makes it possible to generate speech according to the user's preferences.

[0038] For the hearing assistance system, the response generation unit will develop a generative AI that has a deeper understanding of the context of the conversation and can respond to complex questions and requests. The response generation unit will, for example, analyze the context of the conversation and develop a generative AI that can respond to complex questions and requests. For example, it will generate appropriate responses even in conversations that mix multiple topics. The response generation unit will also train the generative AI on large datasets to gain a deeper understanding of the context of the conversation. For example, it will improve its ability to understand the context based on past conversation data. The response generation unit will also introduce advanced natural language processing technology into the generative AI to respond to complex questions and requests. For example, it will accurately understand the intent of the question and generate appropriate responses. This will enable the development of a generative AI that can respond to complex questions and requests.

[0039] The hearing assistance system adds a function in which the response generation unit references the user's past conversation history and generates personalized responses. The response generation unit, for example, references the user's past conversation history and builds a system that generates personalized responses. For example, it customizes responses based on the user's preferences and past comments. The response generation unit also introduces an algorithm that analyzes the conversation history and understands the user's personality and interests. For example, it generates responses based on topics the user frequently talks about. The response generation unit also adds a function in which the response generation unit references the user's past conversation history in real time and generates personalized responses. For example, it provides appropriate responses based on the content of past conversations. This makes it possible to reference the user's past conversation history and generate personalized responses.

[0040] In the hearing assistance system, the response generation unit applies language understanding and response generation to the generation of visual notes and mind maps, enabling visual dialogue. The response generation unit, for example, applies language understanding and response generation to visual notes to build a system that realizes visual dialogue. For example, it automatically converts the content of a conversation into a visual note. The response generation unit also adds a function that uses generation AI to display the content of a conversation in mind map format. For example, it visually organizes the topics of the conversation and related information. The response generation unit also applies language understanding and response generation to visual notes and mind maps, allowing users to visually understand the content of the conversation. For example, it indicates important points with diagrams or icons. This makes visual dialogue using visual notes and mind maps possible.

[0041] The hearing assistance system adds a function in which the response generation unit uses a generation AI to provide advice based on the user's health condition and lifestyle habits. The response generation unit, for example, uses a generation AI to build a system that provides advice based on the user's health condition and lifestyle habits. For example, it provides advice regarding diet and exercise. The response generation unit also introduces an algorithm that analyzes the user's health data and generates personalized advice based on that data. For example, it provides advice based on sleep patterns. The response generation unit also adds a function that uses a generation AI to provide advice based on the user's lifestyle habits in real time. For example, it provides health advice based on daily activities. This makes it possible to provide advice based on the user's health condition and lifestyle habits.

[0042] A hearing assistance system adds functionality that enables seamless two-way communication between multiple devices. For example, a hearing assistance system builds a system that enables seamless two-way communication between multiple devices. For example, it realizes data synchronization between smartphones, tablets, and PCs. A hearing assistance system also develops protocols for seamless communication between multiple devices. For example, it uses Bluetooth or Wi-Fi to transfer data between devices. A hearing assistance system also develops applications that enable seamless two-way communication between multiple devices. For example, it realizes real-time data sharing between devices. This enables seamless two-way communication between multiple devices.

[0043] The hearing assistance system will introduce a function that saves communication history and allows past conversation content to be referenced. For example, the hearing assistance system will build a system that saves communication history and allows past conversation content to be referenced. For example, it will save chat history and audio recordings. The hearing assistance system will also add a function that allows past conversation content to be searched and necessary information to be quickly referenced. For example, it will provide keyword search and filtering functions. The hearing assistance system will also develop a system that saves communication history and visually displays past conversation content. For example, it will display conversation history in a timeline format. In this way, by introducing a function that allows past conversation content to be referenced, communication history will be saved and necessary information will be quickly referenced.

[0044] Hearing assistance systems enable two-way communication in virtual reality (VR) and augmented reality (AR) environments, enabling more realistic conversations. For example, hearing assistance systems build systems that enable two-way communication in virtual reality (VR) environments. For example, realistic conversations are held using a VR headset. In addition, hearing assistance systems develop applications to enable two-way communication in augmented reality (AR) environments. For example, information about the conversation partner is displayed using AR glasses. In addition, hearing assistance systems develop protocols to enable two-way communication in VR and AR environments. For example, real-time data synchronization and voice communication are performed. This enables realistic conversations in virtual reality (VR) and augmented reality (AR) environments.

[0045] The hearing assist system supports two-way communication between different languages ​​and promotes international dialogue. For example, the hearing assist system builds a system that supports two-way communication between different languages. For example, a real-time translation function is introduced to enable dialogue in different languages. The hearing assist system also integrates a translation API to support communication between different languages. For example, real-time translation is performed using the Google (registered trademark) translation API. The hearing assist system also develops an application that supports two-way communication between different languages ​​to promote international dialogue. For example, a multilingual chat app is provided. This supports two-way communication between different languages ​​and promotes international dialogue.

[0046] Hearing assist systems are introduced as guidance systems in public places to improve convenience for people with hearing limitations. Hearing assist systems are introduced, for example, as guidance systems in public places to improve convenience for people with hearing limitations. For example, audio guidance is displayed as text. Hearing assist systems can also be integrated into guidance systems in public places to enable people with hearing limitations to visually confirm information. For example, text guidance is displayed on digital signage. Hearing assist systems can also be introduced as guidance systems in public places to enable people with hearing limitations to move around smoothly. For example, text guidance is provided on train platforms and at airport gates. In this way, introducing them as guidance systems in public places improves convenience for people with hearing limitations.

[0047] The hearing assistance system analyzes usage data and continuously improves its functions according to the user's needs. For example, the hearing assistance system collects usage data and builds a data analysis system to improve its functions according to the user's needs. For example, it analyzes usage frequency and usage patterns. The hearing assistance system also collects user feedback and improves the system's functions based on that data. For example, it analyzes user surveys and reviews. The hearing assistance system also analyzes usage data in real time and continuously improves its functions according to the user's needs. For example, it adds new functions according to usage status. This allows the system to analyze usage data and continuously improve its functions according to the user's needs.

[0048] Hearing assist systems are introduced as assistive tools in educational settings to support the learning of students with hearing limitations. Hearing assist systems are introduced, for example, as assistive tools in educational settings to support the learning of students with hearing limitations. For example, they display the content of lessons in text in real time. Hearing assist systems can also be integrated as assistive tools in educational settings to enable students with hearing limitations to visually check the content of lessons. For example, they convert lecture audio into text. Hearing assist systems can also be introduced as assistive tools in educational settings to enable students with hearing limitations to learn smoothly. For example, they can provide text versions of lecture recordings. By introducing them as assistive tools in educational settings, they can support the learning of students with hearing limitations.

[0049] Hearing assist systems are used as communication tools in medical settings to facilitate dialogue with hearing-limited patients. For example, hearing assist systems are introduced as communication tools in medical settings to facilitate dialogue with hearing-limited patients. For example, they display a doctor's explanations in text. They are also integrated as communication tools in medical settings to allow hearing-limited patients to visually confirm the doctor's explanations. For example, they convert the contents of a medical examination into text. They are also introduced as communication tools in medical settings to enable hearing-limited patients to receive medical services smoothly. For example, they provide prescription explanations in text form. By using them as communication tools in medical settings, they facilitate dialogue with hearing-limited patients.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] In a hearing assistance system, the speech capture unit automatically removes background noise from the speech data and implements an algorithm to achieve more accurate text conversion. For example, noise filtering technology is used to achieve accurate text conversion even in noisy environments. The speech capture unit also applies noise reduction technology as preprocessing of the speech data and sends clear speech data to the analysis unit. For example, echo cancellation and noise gates are used to improve the quality of the speech. The speech capture unit also trains a machine learning model to remove background noise from the speech data and improves its ability to distinguish between noise and speech. For example, the model is trained using noise samples and then applied to actual speech data. This removes background noise, enabling more accurate text conversion.

[0052] In a hearing assistance system, a text generation unit identifies the speaker of an audio signal and applies a different text style to each speaker. For example, it analyzes the speaker's voice characteristics and generates an individual text format. The text generation unit also uses speaker identification technology to analyze a conversation involving multiple speakers and distinguish between each speaker's utterances before converting them into text. For example, when automatically generating meeting minutes, it clearly distinguishes between each speaker's utterances. The text generation unit also extracts speaker characteristics from the audio data and applies individual style templates to apply different text styles to each speaker. For example, it distinguishes between formal and casual conversations. This allows for clear distinction between speakers even when there are multiple speakers by applying different text styles to each speaker.

[0053] In hearing assistance systems, the text generation unit applies speech-to-text conversion to the conversion of sign language and gesture movements into text. For example, a camera captures sign language and gesture movements and an algorithm is introduced to convert those movements into text. For example, the sign language movements are analyzed in real time and the corresponding text is generated. The text generation unit also trains a machine learning model to analyze sign language and gesture movements and uses that model to perform text conversion. For example, sign language movements are collected as a dataset and the model is trained. The text generation unit also develops a dedicated device to convert sign language and gesture movements into text and links that device to the analysis unit. For example, a sensor detects hand movements and converts them into text. This makes it possible to convert sign language and gesture movements into text.

[0054] In a hearing assistance system, the text generation unit translates voice data in real time, enabling text conversion between different languages. For example, an algorithm is introduced to translate voice data in real time, enabling text conversion between different languages. For example, English voice is converted into Japanese text. The text generation unit then analyzes the voice data and builds a system that translates it into different languages ​​in real time via a translation API. For example, it displays text in multiple languages ​​during a meeting. The text generation unit also trains a machine learning model to translate voice data in real time, and links that model to the analysis unit. For example, a system is developed that performs voice recognition and translation simultaneously. This enables text conversion between different languages.

[0055] In a hearing assistance system, the speech generation unit automatically adjusts the tone and speed of speech according to the content of the text. For example, an algorithm is introduced that analyzes the content of the text and automatically adjusts the tone and speed of speech according to that content. For example, the tone is raised to emphasize important parts. The speech generation unit also builds a system that understands the context of the text and selects an appropriate tone and speed of speech. For example, an interrogative tone is applied to questions. The speech generation unit also adds a function to adjust the tone and speed of speech in real time based on the content of the text. For example, an emotive tone is applied to emotional content. This makes it possible to adjust the tone and speed of speech according to the content of the text.

[0056] The hearing assistance system incorporates an algorithm in which the speech generation unit understands the context of the text and generates speech with appropriate intonation and emotion. For example, an emotionally rich speech is applied to an emotional scene. The speech generation unit also builds a system that understands the content of the text and generates speech with appropriate intonation and emotion. For example, different emotions are expressed for each character in a story reading. The speech generation unit also adds a function to analyze the context of the text and generate speech with appropriate intonation and emotion in real time. For example, the tone of the speech is adjusted according to changes in emotion. This makes it possible to understand the context of the text and generate speech with appropriate intonation and emotion.

[0057] In hearing assistance systems, the speech generation unit applies text-to-speech conversion to the conversion of visual information (e.g., images or videos) into speech. For example, an algorithm is introduced that analyzes the content of images or videos and converts that content into speech. For example, a description of the image is generated and converted into speech. The speech generation unit also builds a system that analyzes visual information, converts it into text, and then converts it into speech. For example, it generates subtitles for videos and converts them into speech. The speech generation unit also adds a function that analyzes the content of images or videos in real time and converts that content into speech. For example, the content of live video can be explained in audio. This makes it possible to convert visual information into speech as well.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The voice acquisition unit acquires voice. For example, the voice acquisition unit captures surrounding voice using a microphone. The voice acquisition unit can also use the built-in microphone of a smartphone or tablet. The voice acquisition unit can also acquire voice using a Bluetooth microphone. For example, the voice acquisition unit acquires voice during conversation in real time and transmits it to the system. Step 2: The text generation unit converts the voice acquired by the voice acquisition unit into text. For example, the text generation unit analyzes the voice data using OpenAI's Whisper API and generates corresponding text. The text generation unit can also convert voice into text using speech recognition technology. The text generation unit can also convert voice data into text in real time. For example, the text generation unit converts the voice of a conversation into text in real time and displays it on a screen. Step 3: The speech generation unit converts the text generated by the text generation 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 speech synthesis technology. The speech generation unit can also convert text data into speech in real time. For example, the speech generation unit converts text entered by a user into speech in real time and outputs it from a speaker. Step 4: The response generation unit understands the content of the dialogue and generates an appropriate response. For example, the response generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to understand the context of the dialogue and generate an appropriate response. The response generation unit can also generate a response based on user input. The response generation unit can also analyze the content of the dialogue and generate an appropriate response. For example, the response generation unit generates an appropriate response to a question entered by the user and outputs it as voice via the voice generation unit.

[0060] (Example 2) The hearing aid system according to the embodiment of the present invention is a system that converts speech into text and vice versa, thereby enabling people with hearing limitations to communicate smoothly in their daily lives.

[0061] A hearing assistance system according to an embodiment includes a voice capture unit, a text generation unit, a speech generation unit, and a response generation unit. The voice capture unit captures voice. For example, the voice capture unit captures surrounding voice using a microphone. Alternatively, the voice capture unit may use a built-in microphone of a smartphone or tablet. Alternatively, the voice capture unit may capture voice using a Bluetooth microphone. For example, the voice capture unit captures voice during a conversation in real time and transmits the voice to the system. The text generation unit converts the voice captured by the voice capture unit into text. For example, the text generation unit analyzes voice data using OpenAI's Whisper API and generates corresponding text. Alternatively, the text generation unit may convert voice into text using speech recognition technology. Alternatively, the text generation unit may convert voice data into text in real time. For example, the text generation unit converts voice during a conversation into text in real time and displays it on a screen. The voice generation unit converts the text generated by the text generation unit into voice. For example, the voice generation unit converts text into voice using a Text-to-Speech (TTS) API. The speech generation unit can also convert text into speech using speech synthesis technology. The speech generation unit can also convert text data into speech in real time. For example, the speech generation unit converts text input by a user into speech in real time and outputs it from a speaker. The response generation unit understands the content of a dialogue and generates an appropriate response. For example, the response generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to understand the context of the dialogue and generate an appropriate response. The response generation unit can also generate a response based on a user's input. The response generation unit can also analyze the content of the dialogue and generate an appropriate response. For example, the response generation unit generates an appropriate response to a question input by a user and outputs it as speech via the speech generation unit. This enables people with hearing limitations to communicate smoothly in their daily lives. For example, people with hearing limitations can understand what others are saying in text and express their opinions through speech.It also allows people with hearing impairments to understand the content of conversations and generate appropriate responses, allowing them to enjoy social life more freely.

[0062] In the hearing assistance system, the speech capture unit automatically removes background noise from the speech data and implements an algorithm to enable more accurate text conversion. For example, the speech capture unit analyzes speech data in real time and implements an algorithm to automatically remove background noise. For example, noise filtering technology is used to achieve accurate text conversion even in noisy environments. The speech capture unit also applies noise reduction technology as preprocessing for the speech data and sends clear speech data to the Whisper API. For example, echo cancellation and noise gates are used to improve the quality of the speech. The speech capture unit also trains a machine learning model to remove background noise from the speech data and improves its ability to distinguish between noise and speech. For example, the model is trained using noise samples and then applied to actual speech data. This removes background noise, enabling more accurate text conversion.

[0063] In a hearing assistance system, a text generation unit identifies the speaker of an audio signal and applies a different text style to each speaker. The text generation unit, for example, employs an algorithm to identify speakers from audio data and applies a different text style to each speaker. For example, it analyzes the speaker's voice characteristics and generates an individual text format. The text generation unit also uses speaker identification technology to analyze a conversation involving multiple speakers and distinguish and convert each speaker's utterances into text. For example, when automatically generating meeting minutes, the utterances of each speaker are clearly distinguished. The text generation unit also extracts speaker characteristics from the audio data and applies individual style templates to apply a different text style to each speaker. For example, it distinguishes between formal and casual conversations. This allows for clear distinction between speakers even when there are multiple speakers by applying different text styles to each speaker.

[0064] In a hearing assistance system, a text generator uses an emotion estimation function to reflect the speaker's emotion in the text, thereby achieving text conversion that incorporates emotional nuances. The text generator, for example, incorporates an algorithm that estimates the speaker's emotion from audio data and reflects that emotion in the text. For example, it analyzes the tone and pitch of the voice and adds emotion tags to the text. The text generator also uses emotion estimation technology to analyze the speaker's emotion in real time and converts the text to incorporate emotional nuances. For example, emotions such as joy and sadness are reflected in the text. The text generator also estimates the emotion from the audio data and applies a text style according to the emotion. For example, emotional nuances are visually expressed by highlighting parts with strong emotions. This enables text conversion that incorporates emotional nuances.

[0065] In the hearing assistance system, the text generation unit applies speech-to-text conversion to the conversion of sign language and gesture movements into text. For example, the text generation unit captures sign language and gesture movements with a camera and implements an algorithm to convert those movements into text. For example, it analyzes sign language movements in real time and generates corresponding text. The text generation unit also trains a machine learning model to analyze sign language and gesture movements and uses that model to perform text conversion. For example, it collects sign language movements as a dataset and trains the model. The text generation unit also develops a dedicated device to convert sign language and gesture movements into text and links that device to the Whisper API. For example, it detects hand movements with a sensor and converts them into text. This makes it possible to convert sign language and gesture movements into text.

[0066] In a hearing assistance system, the text generation unit translates voice data in real time, enabling text conversion between different languages. The text generation unit, for example, introduces an algorithm that translates voice data in real time to achieve text conversion between different languages. For example, it converts English voice into Japanese text. The text generation unit also analyzes voice data and builds a system that translates it into different languages ​​in real time via a translation API. For example, it displays text in multiple languages ​​during a meeting. The text generation unit also trains a machine learning model for translating voice data in real time and links the model to the Whisper API. For example, it develops a system that performs voice recognition and translation simultaneously. This enables text conversion between different languages.

[0067] In a hearing assistance system, a text generation unit uses an emotion estimation function to display a speaker's emotion in real time during speech-to-text conversion, allowing the user to visually confirm changes in emotion. The text generation unit, for example, builds a system that estimates a speaker's emotion from speech data in real time and displays that emotion together with the text. For example, an emotion score is displayed next to the text. The text generation unit also uses emotion estimation technology to visually display a speaker's emotion during speech-to-text conversion. For example, the intensity of the emotion is indicated by color or an icon. The text generation unit also develops a system that estimates emotion from speech data and displays changes in emotion in real time. For example, changes in emotion are displayed as graphs or animations. This allows the user to visually confirm changes in the speaker's emotion.

[0068] In a hearing assistance system, a speech generation unit automatically adjusts the tone and speed of speech according to the content of the text. For example, the speech generation unit implements an algorithm that analyzes the content of the text and automatically adjusts the tone and speed of speech according to that content. For example, the tone may be raised to emphasize important parts. The speech generation unit also builds a system that understands the context of the text and selects an appropriate tone and speed of speech. For example, an interrogative tone may be applied to questions. The speech generation unit also adds a function to adjust the tone and speed of speech in real time based on the content of the text. For example, an emotional tone may be applied to emotional content. This makes it possible to adjust the tone and speed of speech according to the content of the text.

[0069] The hearing assistance system incorporates an algorithm in which the speech generation unit understands the context of the text and generates speech with appropriate intonation and emotion. The speech generation unit incorporates, for example, an algorithm that analyzes the context of the text and generates speech with appropriate intonation and emotion. For example, emotionally rich speech is applied to emotional scenes. The speech generation unit also constructs a system that understands the content of the text and generates speech with appropriate intonation and emotion. For example, different emotions are expressed for each character in a story reading. The speech generation unit also adds a function that analyzes the context of the text and generates speech with appropriate intonation and emotion in real time. For example, the tone of the speech is adjusted according to changes in emotion. This makes it possible to understand the context of the text and generate speech with appropriate intonation and emotion.

[0070] In the hearing assistance system, the voice generation unit uses an emotion estimation function to select a voice style according to the user's emotion, thereby generating more natural voice. The voice generation unit, for example, uses the emotion estimation function to build a system that selects a voice style according to the user's emotion. For example, if the user is happy, it generates a voice with a bright tone. The voice generation unit also introduces an algorithm that analyzes the user's emotion in real time and selects a voice style according to that emotion. For example, if the user is sad, it generates a voice with a calm tone. The voice generation unit also adds a function that selects a voice style according to the user's emotion in real time based on the emotion estimation data. For example, it adjusts the tone and speed of the voice according to changes in emotion. This allows for the selection of a voice style according to the user's emotion, thereby generating more natural voice.

[0071] In hearing assistance systems, the speech generation unit applies text-to-speech conversion to the conversion of visual information (e.g., images or videos) into speech. The speech generation unit, for example, introduces an algorithm that analyzes the content of images or videos and converts that content into speech. For example, it generates a description of the image and converts it into speech. The speech generation unit also builds a system that analyzes visual information, converts it into text, and then converts it into speech. For example, it generates subtitles for videos and converts them into speech. The speech generation unit also adds a function that analyzes the content of images or videos in real time and converts that content into speech. For example, it explains the content of live video in audio. This makes it possible to convert visual information into speech as well.

[0072] The hearing assistance system adds a function that allows the voice generation unit to select different character voices when converting text to speech, thereby enabling speech generation according to the user's preferences. For example, the speech generation unit adds a function that allows the voice generation unit to select different character voices when converting text to speech. For example, it makes it possible to select male voices, female voices, children's voices, etc. The speech generation unit also builds a system that allows the voice generation unit to select different character voices according to the user's preferences. For example, it makes it possible to select the voice of an anime character. The speech generation unit also adds a function that allows the voice generation unit to select different character voices in real time when converting text to speech. For example, it makes it possible for the user to listen to voice samples and make a selection. This makes it possible to generate speech according to the user's preferences.

[0073] In the hearing assistance system, the speech generation unit uses an emotion estimation function to reflect the user's emotions during text-to-speech conversion, generating emotionally rich speech. The speech generation unit, for example, uses the emotion estimation function to build a system that reflects the user's emotions during text-to-speech conversion. For example, if the user is happy, it generates a bright-toned speech. The speech generation unit also introduces an algorithm that analyzes the user's emotions in real time and generates speech that reflects those emotions. For example, if the user is sad, it generates a calm-toned speech. The speech generation unit also adds a function to generate emotionally rich speech during text-to-speech conversion based on the emotion estimation data. For example, it adjusts the tone and speed of the speech according to changes in emotion. This makes it possible to generate emotionally rich speech that reflects the user's emotions.

[0074] For the hearing assistance system, the response generation unit will develop a generative AI that has a deeper understanding of the context of the conversation and can respond to complex questions and requests. The response generation unit will, for example, analyze the context of the conversation and develop a generative AI that can respond to complex questions and requests. For example, it will generate appropriate responses even in conversations that mix multiple topics. The response generation unit will also train the generative AI on large datasets to gain a deeper understanding of the context of the conversation. For example, it will improve its ability to understand the context based on past conversation data. The response generation unit will also introduce advanced natural language processing technology into the generative AI to respond to complex questions and requests. For example, it will accurately understand the intent of the question and generate appropriate responses. This will enable the development of a generative AI that can respond to complex questions and requests.

[0075] The hearing assistance system adds a function in which the response generation unit references the user's past conversation history and generates personalized responses. The response generation unit, for example, references the user's past conversation history and builds a system that generates personalized responses. For example, it customizes responses based on the user's preferences and past comments. The response generation unit also introduces an algorithm that analyzes the conversation history and understands the user's personality and interests. For example, it generates responses based on topics the user frequently talks about. The response generation unit also adds a function in which the response generation unit references the user's past conversation history in real time and generates personalized responses. For example, it provides appropriate responses based on the content of past conversations. This makes it possible to reference the user's past conversation history and generate personalized responses.

[0076] In the hearing assistance system, the response generation unit uses an emotion estimation function to generate appropriate responses according to the user's emotions, realizing more empathetic dialogue. The response generation unit, for example, uses the emotion estimation function to build a system that generates appropriate responses according to the user's emotions. For example, if the user is sad, it provides words of encouragement. The response generation unit also introduces an algorithm that analyzes the user's emotions in real time and generates responses according to those emotions. For example, if the user is happy, it provides words of empathy. The response generation unit also adds a function that generates responses according to the user's emotions in real time based on the emotion estimation data. For example, it adjusts the tone and content of the response according to changes in emotion. This allows appropriate responses to be generated according to the user's emotions, realizing more empathetic dialogue.

[0077] In the hearing assistance system, the response generation unit applies language understanding and response generation to the generation of visual notes and mind maps, enabling visual dialogue. The response generation unit, for example, applies language understanding and response generation to visual notes to build a system that realizes visual dialogue. For example, it automatically converts the content of a conversation into a visual note. The response generation unit also adds a function that uses generation AI to display the content of a conversation in mind map format. For example, it visually organizes the topics of the conversation and related information. The response generation unit also applies language understanding and response generation to visual notes and mind maps, allowing users to visually understand the content of the conversation. For example, it indicates important points with diagrams or icons. This makes visual dialogue using visual notes and mind maps possible.

[0078] The hearing assistance system adds a function in which the response generation unit uses a generation AI to provide advice based on the user's health condition and lifestyle habits. The response generation unit, for example, uses a generation AI to build a system that provides advice based on the user's health condition and lifestyle habits. For example, it provides advice regarding diet and exercise. The response generation unit also introduces an algorithm that analyzes the user's health data and generates personalized advice based on that data. For example, it provides advice based on sleep patterns. The response generation unit also adds a function that uses a generation AI to provide advice based on the user's lifestyle habits in real time. For example, it provides health advice based on daily activities. This makes it possible to provide advice based on the user's health condition and lifestyle habits.

[0079] In the hearing assistance system, the response generation unit uses an emotion estimation function to allow the generation AI to monitor the user's emotions in real time and generate responses in response to changes in emotion. The response generation unit, for example, uses the emotion estimation function to build a system in which the generation AI monitors the user's emotions in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The response generation unit also introduces an algorithm that analyzes the user's emotions in real time and generates responses in response to those emotions. For example, if the user is angry, it provides a calm response. The response generation unit also adds a function to generate responses in real time in response to the user's emotions based on the emotion estimation data. For example, it adjusts the tone and content of the response in response to changes in emotion. This makes it possible to monitor the user's emotions in real time and generate responses in response to changes in emotion.

[0080] A hearing assistance system adds functionality that enables seamless two-way communication between multiple devices. For example, a hearing assistance system builds a system that enables seamless two-way communication between multiple devices. For example, it realizes data synchronization between smartphones, tablets, and PCs. A hearing assistance system also develops protocols for seamless communication between multiple devices. For example, it uses Bluetooth or Wi-Fi to transfer data between devices. A hearing assistance system also develops applications that enable seamless two-way communication between multiple devices. For example, it realizes real-time data sharing between devices. This enables seamless two-way communication between multiple devices.

[0081] The hearing assistance system will introduce a function that saves communication history and allows past conversation content to be referenced. For example, the hearing assistance system will build a system that saves communication history and allows past conversation content to be referenced. For example, it will save chat history and audio recordings. The hearing assistance system will also add a function that allows past conversation content to be searched and necessary information to be quickly referenced. For example, it will provide keyword search and filtering functions. The hearing assistance system will also develop a system that saves communication history and visually displays past conversation content. For example, it will display conversation history in a timeline format. In this way, by introducing a function that allows past conversation content to be referenced, communication history will be saved and necessary information will be quickly referenced.

[0082] The hearing assistance system uses an emotion estimation function to display a user's emotions in real time during two-way communication and share emotional changes. For example, the hearing assistance system uses the emotion estimation function to build a system that displays a user's emotions in real time during two-way communication. For example, an emotion score is displayed in a chat window. The hearing assistance system also adds a function that analyzes a user's emotions in real time and visually displays changes in those emotions. For example, the changes in emotions are shown using graphs or icons. The hearing assistance system also develops a system that shares a user's emotions during two-way communication based on the emotion estimation data. For example, the system notifies the other party of changes in emotions in real time. This allows a user's emotions to be displayed in real time during two-way communication and emotional changes to be shared.

[0083] Hearing assistance systems enable two-way communication in virtual reality (VR) and augmented reality (AR) environments, enabling more realistic conversations. For example, hearing assistance systems build systems that enable two-way communication in virtual reality (VR) environments. For example, realistic conversations are held using a VR headset. In addition, hearing assistance systems develop applications to enable two-way communication in augmented reality (AR) environments. For example, information about the conversation partner is displayed using AR glasses. In addition, hearing assistance systems develop protocols to enable two-way communication in VR and AR environments. For example, real-time data synchronization and voice communication are performed. This enables realistic conversations in virtual reality (VR) and augmented reality (AR) environments.

[0084] The hearing assist system supports two-way communication between different languages ​​and promotes international dialogue. For example, the hearing assist system builds a system that supports two-way communication between different languages. For example, a real-time translation function is introduced to enable dialogue in different languages. The hearing assist system also integrates a translation API to support communication between different languages. For example, real-time translation is performed using the Google (registered trademark) translation API. The hearing assist system also develops an application that supports two-way communication between different languages ​​to promote international dialogue. For example, a multilingual chat app is provided. This supports two-way communication between different languages ​​and promotes international dialogue.

[0085] The hearing assistance system uses an emotion estimation function to analyze a user's emotions during two-way communication and provide feedback based on the emotions. For example, the hearing assistance system uses the emotion estimation function to build a system that analyzes a user's emotions during two-way communication. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. The hearing assistance system also introduces an algorithm that analyzes the user's emotions in real time and provides feedback based on the emotions. For example, if the user is nervous, the system provides advice on how to relax. The hearing assistance system also adds a function to provide emotion-based feedback during two-way communication based on the emotion estimation data. For example, the system adjusts the tone and content of the conversation according to changes in emotions. This makes it possible to analyze a user's emotions during two-way communication and provide emotion-based feedback.

[0086] Hearing assist systems are introduced as guidance systems in public places to improve convenience for people with hearing limitations. Hearing assist systems are introduced, for example, as guidance systems in public places to improve convenience for people with hearing limitations. For example, audio guidance is displayed as text. Hearing assist systems can also be integrated into guidance systems in public places to enable people with hearing limitations to visually confirm information. For example, text guidance is displayed on digital signage. Hearing assist systems can also be introduced as guidance systems in public places to enable people with hearing limitations to move around smoothly. For example, text guidance is provided on train platforms and at airport gates. In this way, introducing them as guidance systems in public places improves convenience for people with hearing limitations.

[0087] The hearing assistance system analyzes usage data and continuously improves its functions according to the user's needs. For example, the hearing assistance system collects usage data and builds a data analysis system to improve its functions according to the user's needs. For example, it analyzes usage frequency and usage patterns. The hearing assistance system also collects user feedback and improves the system's functions based on that data. For example, it analyzes user surveys and reviews. The hearing assistance system also analyzes usage data in real time and continuously improves its functions according to the user's needs. For example, it adds new functions according to usage status. This allows the system to analyze usage data and continuously improve its functions according to the user's needs.

[0088] The hearing assistance system uses an emotion estimation function to monitor changes in emotions in a user's social life and provide support based on those emotions. For example, the hearing assistance system uses the emotion estimation function to build a system that monitors changes in emotions in a user's social life. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The hearing assistance system also introduces an algorithm that analyzes the user's emotions in real time and provides support based on those emotions. For example, if the user is feeling stressed, it provides advice on how to relax. The hearing assistance system also adds a function that monitors changes in emotions in a user's social life based on the emotion estimation data and provides support based on those emotions. For example, it adjusts the support content according to changes in emotions. This makes it possible to monitor changes in emotions in a user's social life and provide support based on those emotions.

[0089] Hearing assist systems are introduced as assistive tools in educational settings to support the learning of students with hearing limitations. Hearing assist systems are introduced, for example, as assistive tools in educational settings to support the learning of students with hearing limitations. For example, they display the content of lessons in text in real time. Hearing assist systems can also be integrated as assistive tools in educational settings to enable students with hearing limitations to visually check the content of lessons. For example, they convert lecture audio into text. Hearing assist systems can also be introduced as assistive tools in educational settings to enable students with hearing limitations to learn smoothly. For example, they can provide text versions of lecture recordings. By introducing them as assistive tools in educational settings, they can support the learning of students with hearing limitations.

[0090] Hearing assist systems are used as communication tools in medical settings to facilitate dialogue with hearing-limited patients. For example, hearing assist systems are introduced as communication tools in medical settings to facilitate dialogue with hearing-limited patients. For example, they display a doctor's explanations in text. They are also integrated as communication tools in medical settings to allow hearing-limited patients to visually confirm the doctor's explanations. For example, they convert the contents of a medical examination into text. They are also introduced as communication tools in medical settings to enable hearing-limited patients to receive medical services smoothly. For example, they provide prescription explanations in text form. By using them as communication tools in medical settings, they facilitate dialogue with hearing-limited patients.

[0091] The hearing assistance system uses an emotion estimation function to analyze a user's emotions in social life and proposes measures to promote social participation based on the emotions. The hearing assistance system, for example, uses the emotion estimation function to build a system that analyzes a user's emotions in social life. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The hearing assistance system also introduces an algorithm that analyzes the user's emotions in real time and proposes measures to promote social participation based on those emotions. For example, if the user feels lonely, it suggests community activities. The hearing assistance system also adds a function that analyzes the user's emotions in social life based on the emotion estimation data and proposes measures to promote social participation based on the emotions. For example, it suggests events to participate in depending on changes in emotions. This makes it possible to analyze the user's emotions in social life and propose measures to promote social participation based on the emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] In a hearing assistance system, the speech capture unit automatically removes background noise from the speech data and implements an algorithm to achieve more accurate text conversion. For example, noise filtering technology is used to achieve accurate text conversion even in noisy environments. The speech capture unit also applies noise reduction technology as preprocessing of the speech data and sends clear speech data to the analysis unit. For example, echo cancellation and noise gates are used to improve the quality of the speech. The speech capture unit also trains a machine learning model to remove background noise from the speech data and improves its ability to distinguish between noise and speech. For example, the model is trained using noise samples and then applied to actual speech data. This removes background noise, enabling more accurate text conversion.

[0094] In a hearing assistance system, a text generation unit identifies the speaker of an audio signal and applies a different text style to each speaker. For example, it analyzes the speaker's voice characteristics and generates an individual text format. The text generation unit also uses speaker identification technology to analyze a conversation involving multiple speakers and distinguish between each speaker's utterances before converting them into text. For example, when automatically generating meeting minutes, it clearly distinguishes between each speaker's utterances. The text generation unit also extracts speaker characteristics from the audio data and applies individual style templates to apply different text styles to each speaker. For example, it distinguishes between formal and casual conversations. This allows for clear distinction between speakers even when there are multiple speakers by applying different text styles to each speaker.

[0095] In the hearing assistance system, the text generation unit uses an emotion estimation function to reflect the speaker's emotions in the text, thereby achieving text conversion that incorporates emotional nuances. For example, it analyzes the tone and pitch of the voice and adds emotion tags to the text. The text generation unit also uses emotion estimation technology to analyze the speaker's emotions in real time and converts the text to incorporate emotional nuances. For example, emotions such as joy and sadness are reflected in the text. The text generation unit also estimates emotions from the voice data and applies a text style according to the emotion. For example, emotional nuances are visually expressed by highlighting parts with strong emotions. This makes it possible to convert text to incorporate emotional nuances.

[0096] In hearing assistance systems, the text generation unit applies speech-to-text conversion to the conversion of sign language and gesture movements into text. For example, a camera captures sign language and gesture movements and an algorithm is introduced to convert those movements into text. For example, the sign language movements are analyzed in real time and the corresponding text is generated. The text generation unit also trains a machine learning model to analyze sign language and gesture movements and uses that model to perform text conversion. For example, sign language movements are collected as a dataset and the model is trained. The text generation unit also develops a dedicated device to convert sign language and gesture movements into text and links that device to the analysis unit. For example, a sensor detects hand movements and converts them into text. This makes it possible to convert sign language and gesture movements into text.

[0097] In a hearing assistance system, the text generation unit translates voice data in real time, enabling text conversion between different languages. For example, an algorithm is introduced to translate voice data in real time, enabling text conversion between different languages. For example, English voice is converted into Japanese text. The text generation unit then analyzes the voice data and builds a system that translates it into different languages ​​in real time via a translation API. For example, it displays text in multiple languages ​​during a meeting. The text generation unit also trains a machine learning model to translate voice data in real time, and links that model to the analysis unit. For example, a system is developed that performs voice recognition and translation simultaneously. This enables text conversion between different languages.

[0098] In a hearing assistance system, the text generation unit uses an emotion estimation function to display the speaker's emotion in real time during speech-to-text conversion, allowing the user to visually confirm changes in emotion. For example, a system is constructed that estimates the speaker's emotion from speech data in real time and displays that emotion together with the text. For example, an emotion score is displayed next to the text. The text generation unit also uses emotion estimation technology to visually display the speaker's emotion during speech-to-text conversion. For example, the intensity of the emotion is indicated by color or an icon. The text generation unit also develops a system that estimates emotion from speech data and displays changes in emotion in real time. For example, changes in emotion are displayed in graphs or animations. This allows the user to visually confirm changes in the speaker's emotion.

[0099] In a hearing assistance system, the speech generation unit automatically adjusts the tone and speed of speech according to the content of the text. For example, an algorithm is introduced that analyzes the content of the text and automatically adjusts the tone and speed of speech according to that content. For example, the tone is raised to emphasize important parts. The speech generation unit also builds a system that understands the context of the text and selects an appropriate tone and speed of speech. For example, an interrogative tone is applied to questions. The speech generation unit also adds a function to adjust the tone and speed of speech in real time based on the content of the text. For example, an emotive tone is applied to emotional content. This makes it possible to adjust the tone and speed of speech according to the content of the text.

[0100] The hearing assistance system incorporates an algorithm in which the speech generation unit understands the context of the text and generates speech with appropriate intonation and emotion. For example, an emotionally rich speech is applied to an emotional scene. The speech generation unit also builds a system that understands the content of the text and generates speech with appropriate intonation and emotion. For example, different emotions are expressed for each character in a story reading. The speech generation unit also adds a function to analyze the context of the text and generate speech with appropriate intonation and emotion in real time. For example, the tone of the speech is adjusted according to changes in emotion. This makes it possible to understand the context of the text and generate speech with appropriate intonation and emotion.

[0101] In a hearing assistance system, the speech generation unit uses an emotion estimation function to select a speech style that corresponds to the user's emotion, thereby generating more natural speech. For example, a system is constructed that uses the emotion estimation function to select a speech style that corresponds to the user's emotion. For example, if the user is happy, a bright tone of speech is generated. The speech generation unit also introduces an algorithm that analyzes the user's emotion in real time and selects a speech style that corresponds to that emotion. For example, if the user is sad, a calm tone of speech is generated. The speech generation unit also adds a function to select a speech style that corresponds to the user's emotion in real time based on the emotion estimation data. For example, the tone and speed of the speech are adjusted according to changes in emotion. This allows a speech style to be selected that corresponds to the user's emotion, enabling more natural speech to be generated.

[0102] In hearing assistance systems, the speech generation unit applies text-to-speech conversion to the conversion of visual information (e.g., images or videos) into speech. For example, an algorithm is introduced that analyzes the content of images or videos and converts that content into speech. For example, a description of the image is generated and converted into speech. The speech generation unit also builds a system that analyzes visual information, converts it into text, and then converts it into speech. For example, it generates subtitles for videos and converts them into speech. The speech generation unit also adds a function that analyzes the content of images or videos in real time and converts that content into speech. For example, the content of live video can be explained in audio. This makes it possible to convert visual information into speech as well.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The voice acquisition unit acquires voice. For example, the voice acquisition unit captures surrounding voice using a microphone. The voice acquisition unit can also use the built-in microphone of a smartphone or tablet. The voice acquisition unit can also acquire voice using a Bluetooth microphone. For example, the voice acquisition unit acquires voice during conversation in real time and transmits it to the system. Step 2: The text generation unit converts the voice acquired by the voice acquisition unit into text. For example, the text generation unit analyzes the voice data using OpenAI's Whisper API and generates corresponding text. The text generation unit can also convert voice into text using speech recognition technology. The text generation unit can also convert voice data into text in real time. For example, the text generation unit converts the voice of a conversation into text in real time and displays it on a screen. Step 3: The speech generation unit converts the text generated by the text generation 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 speech synthesis technology. The speech generation unit can also convert text data into speech in real time. For example, the speech generation unit converts text entered by a user into speech in real time and outputs it from a speaker. Step 4: The response generation unit understands the content of the dialogue and generates an appropriate response. For example, the response generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to understand the context of the dialogue and generate an appropriate response. The response generation unit can also generate a response based on user input. The response generation unit can also analyze the content of the dialogue and generate an appropriate response. For example, the response generation unit generates an appropriate response to a question entered by the user and outputs it as voice via the voice generation unit.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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 speech generation unit that converts the text generated by the text generation unit into speech; A response generation unit that understands the content of the dialogue and generates an appropriate response. A system characterized by:

2. The voice acquisition unit Introducing an algorithm that automatically removes background noise from voice data and provides more accurate text conversion.

2. The system of claim 1.

3. The text generation unit Identifying speakers of the audio and applying different text styles to each speaker 2. The system of claim 1.

4. The text generation unit By reflecting the speaker's emotions in the text, text conversion that includes emotional nuances is realized.

2. The system of claim 1.

5. The text generation unit The conversion of the voice to the text is also applied to the conversion of the sign language or gesture movements to the text.

2. The system of claim 1.

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