System

Abstract

An object of a system according to an embodiment is to enable a user with poor articulation to effectively perform communication.SOLUTION: A system according to an embodiment includes a timbre learning unit, a reading unit, a transcription unit, a document creation unit, and a training unit. The timbre learning unit learns the user's timbre using the generated AI. The reading unit reads the text in the tone of voice learned by the tone of voice learning unit. The transcription unit transcribes the user's voice in real time. The manuscript creation unit creates a manuscript based on the user's pronunciation. The training unit provides training for improving the user's speech.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 techniques have had the problem that it is difficult for users with poor pronunciation to communicate effectively.

[0005] The system according to the embodiment aims to enable users with poor pronunciation to communicate effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a vocal tone learning unit, a reading unit, a transcription unit, a script creation unit, and a training unit. The vocal tone learning unit learns the user's voice using a generation AI. The reading unit reads text in the vocal tone learned by the vocal tone learning unit. The transcription unit transcribes the user's voice in real time. The script creation unit creates a script based on the user's enunciation. The training unit provides training to improve the user's enunciation. [Effects of the Invention]

[0007] The system according to the embodiment allows users with poor pronunciation to communicate effectively. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 articulation support system according to an embodiment of the present invention uses a generation AI to learn the user's voice, read aloud in that tone of voice, transcribe text specifically for that voice, create manuscripts that match the user's articulation, and provide training to improve articulation. As a result, the articulation support system can support people with poor articulation so that they can communicate smoothly in their daily lives.

[0029] The articulation support system according to the embodiment includes a vocal tone learning unit, a reading unit, a transcription unit, a script creation unit, and a training unit. The vocal tone learning unit allows a generation AI to learn the user's voice. For example, the generation AI collects the user's voice data and analyzes and learns the voice characteristics. The generation AI can also use voice synthesis technology to imitate the user's vocal tone. The generation AI can also learn the frequency characteristics and sound quality of the user's voice. The reading unit recites text using the vocal tone learned by the vocal tone learning unit. For example, the generation AI recites text entered by the user in the user's vocal tone. The generation AI can also adjust the intonation and rhythm of the text to read it more naturally. The generation AI can also change the vocal tone depending on the user's emotional state. The transcription unit transcribes the user's voice in real time. For example, the generation AI converts what the user says into text using speech recognition technology. The generation AI can also remove noise from the voice to perform highly accurate transcription. The generation AI can also analyze the intent of the user's speech and add supplementary information to clarify ambiguous expressions. The manuscript creation unit creates a manuscript based on the user's pronunciation. For example, the generation AI can learn the user's pronunciation characteristics and select easy-to-pronounce words to create a manuscript. The generation AI can also analyze the user's past speech data to select natural expressions. The generation AI can also create manuscripts suitable for different situations. The training unit provides training to improve the user's pronunciation. For example, the generation AI can analyze the user's pronunciation and identify areas that need improvement. The generation AI can also suggest specific pronunciation exercises to the user and provide training. The generation AI can also provide a training program tailored to the user's emotional state. As a result, the pronunciation support system according to the embodiment can support people with poor pronunciation in communicating smoothly in everyday life. For example, a user can give a presentation by reading aloud in their own voice or record the contents of a meeting using a dedicated transcription tool.It is also expected that users' speech will improve through the creation of manuscripts suited to their pronunciation and speech improvement training.

[0030] The reading unit can analyze the user's speech data and reproduce the most natural intonation and rhythm. For example, the generation AI of the reading unit collects the user's past speech data and uses speech analysis technology to extract intonation and rhythm patterns. For example, it learns the user's frequently used phrases and expressions. The reading unit can also develop an algorithm that reproduces natural intonation and rhythm based on the user's speech data. For example, it can learn the characteristics of the user's speaking style and recite text based on that. The reading unit can also adjust the intonation and rhythm of the text based on the user's speech data. This allows the user's natural speaking style to be reproduced.

[0031] The reading unit can learn the characteristics of the user's voice and enable reading in different languages ​​and dialects. For example, the reading unit's generation AI learns the characteristics of the user's voice and reproduces the pronunciation and intonation in different languages. For example, reading in multiple languages ​​such as English, Japanese, and French is possible. The reading unit can also read in different dialects based on the characteristics of the user's voice. For example, reading in dialects such as Kansai dialect or Tohoku dialect is possible. The reading unit can also adjust the pronunciation in different languages ​​and dialects based on the characteristics of the user's voice. This makes it possible to read in different languages ​​and dialects.

[0032] The transcription unit can analyze the intent of the user's utterance and add supplementary information to clarify ambiguous expressions. For example, the generation AI in the transcription unit analyzes the user's utterance and identifies ambiguous expressions. For example, it replaces demonstratives such as "that" with specific nouns. The transcription unit can also analyze the intent of the user's utterance and add supplementary information. For example, it replaces ambiguous expressions such as "that place" with specific place names. The transcription unit can also analyze the context of the user's utterance and add appropriate supplementary information. This makes it possible to add supplementary information to clarify ambiguous expressions.

[0033] The transcription unit can analyze the background sounds of the user's speech and remove noise to produce highly accurate transcriptions. For example, the generation AI in the transcription unit analyzes the background sounds of the user's speech and identifies the noise. For example, it detects the sound of wind or cars. The transcription unit can also remove background sounds using a noise removal algorithm. For example, it can filter environmental noise to make the audio clearer. The transcription unit can also remove noise based on the background sounds of the user's speech and produce highly accurate transcriptions. This makes it possible to remove noise and produce highly accurate transcriptions.

[0034] The manuscript creation department can analyze the user's speech data and select the most natural expressions to create the manuscript. For example, the generation AI in the manuscript creation department collects the user's past speech data and develops an algorithm to select natural expressions. For example, it learns the phrases and expressions that users frequently use. The manuscript creation department can also create manuscripts by having the generation AI select natural expressions based on the user's speech data. For example, it can create manuscripts based on phrases used in everyday conversation. The manuscript creation department can also adjust the content of the manuscript based on the user's speech data. This allows the generation AI to create manuscripts by selecting natural expressions for the user.

[0035] The manuscript creation unit can learn the user's enunciation characteristics and create manuscripts appropriate for different situations. For example, the manuscript creation unit will develop a system in which a generation AI learns the user's enunciation characteristics and creates manuscripts appropriate for different situations. For example, it will create manuscripts appropriate for situations such as presentations and meetings. The manuscript creation unit can also have the generation AI select words appropriate for the situation based on the user's enunciation characteristics. For example, formal words will be selected in business situations. The manuscript creation unit can also have the generation AI adjust the structure of the manuscript based on the user's enunciation characteristics. This makes it possible to create manuscripts appropriate for different situations.

[0036] The training department can analyze the user's pronunciation data and create an individually customized training menu. For example, the training department develops a system in which a generation AI analyzes the user's pronunciation data and creates an individually customized training menu. For example, if there is a problem with a particular pronunciation, the training department can focus on practicing that pronunciation. The training department can also adjust the training menu based on the user's pronunciation data using the generation AI. For example, the training content can be changed according to the user's progress. The training department can also adjust the difficulty of the training based on the user's pronunciation data using the generation AI. This makes it possible to create an individually customized training menu.

[0037] The training department can monitor the user's training progress in real time and provide appropriate feedback. For example, the training department develops a system in which the generation AI monitors the user's training progress in real time and provides appropriate feedback. For example, if an improvement in pronunciation is observed, praising feedback is provided. The training department can also adjust the content of the feedback based on the user's training progress using the generation AI. For example, specific advice is provided for areas in which the user is weak. The training department can also adjust the progress of training based on the user's training progress using the generation AI. This allows training progress to be monitored in real time and appropriate feedback to be provided.

[0038] The training department can develop games and apps to help users improve their pronunciation. For example, the training department can develop games that use the generation AI to help users improve their pronunciation. For example, it can provide mini-games that allow users to practice pronunciation in a fun way. The training department can also develop apps that use the generation AI to help users improve their pronunciation. For example, it can provide mobile apps and desktop apps. The training department can also adjust the content of games and apps that use the generation AI to help users improve their pronunciation. This makes it possible to develop games and apps that help users improve their pronunciation.

[0039] The training department can provide group training sessions to help users improve their pronunciation. For example, the training department develops a system in which the generation AI provides group training sessions to help users improve their pronunciation. For example, it provides online sessions in which multiple users can participate simultaneously. The training department can also provide face-to-face sessions in which the generation AI helps users improve their pronunciation. For example, it can conduct group pronunciation practice. The training department can also adjust the content of the session in which the generation AI helps users improve their pronunciation. In this way, it is possible to provide group training sessions to help improve pronunciation.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The articulation support system can also include a health management unit that monitors the user's vocal health. For example, the generative AI can analyze changes in the user's voice and detect vocal fatigue or abnormalities. The health management unit can also provide the user with advice on maintaining vocal health. For example, it can recommend vocal rest or suggest appropriate vocal exercises. The health management unit can also record the user's vocal health and analyze long-term trends. This allows the user to improve their articulation while maintaining vocal health.

[0042] The articulation support system can further include a feedback unit that analyzes the content of a user's speech and provides appropriate feedback. For example, the generation AI analyzes the content of a user's speech and identifies areas for improvement in pronunciation. The feedback unit can also provide the user with specific pronunciation practice advice. For example, it can show how to pronounce a specific sound. The feedback unit can also evaluate the user's pronunciation progress based on the content of the user's speech. This allows the user to improve their articulation while receiving specific feedback.

[0043] The articulation support system can further include a visualization unit that analyzes the user's utterance data and visualizes pronunciation patterns. For example, the generation AI analyzes the user's utterance data and displays pronunciation characteristics in graphs or charts. The visualization unit can also visually show the user areas for improvement in pronunciation. For example, if the pronunciation of a particular sound is unclear, that part can be highlighted. The visualization unit can also visually track the user's pronunciation progress. This allows the user to train while visually checking the progress of their pronunciation.

[0044] The articulation support system can further include a gamification unit that analyzes the user's utterance data and turns pronunciation practice into a game. For example, a generation AI analyzes the user's utterance data and provides pronunciation practice in the form of a game. The gamification unit can also provide mini-games that allow the user to practice pronunciation in a fun way. For example, a game can be provided in which points can be earned by correctly pronouncing specific sounds. The gamification unit can also evaluate the user's pronunciation progress within the game and provide rewards. This allows the user to practice pronunciation in a fun way.

[0045] The articulation support system can further include a teaching material providing unit that analyzes the user's utterance data and provides customized teaching materials to support pronunciation improvement. For example, the generation AI analyzes the user's utterance data and selects teaching materials necessary for pronunciation improvement. The teaching material providing unit can also provide pronunciation practice materials that are individually customized for the user. For example, it can provide teaching materials that focus on practicing the pronunciation of specific sounds. The teaching material providing unit can also adjust the content of the teaching materials based on the user's pronunciation progress. This allows the user to practice pronunciation using teaching materials that suit them.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: In the voice tone learning section, the generation AI learns the user's voice. For example, the generation AI collects the user's voice data and analyzes and learns the voice characteristics. The generation AI can also use voice synthesis technology to imitate the user's voice tone. Furthermore, the generation AI can learn the frequency characteristics and sound quality of the user's voice. Step 2: The reading unit reads the text in the tone of voice learned by the tone of voice learning unit. For example, the generation AI reads the text entered by the user in the user's tone of voice. The generation AI can also adjust the intonation and rhythm of the text to read it more naturally. Furthermore, the generation AI can change the tone of voice depending on the user's emotional state. Step 3: The transcription part transcribes the user's speech in real time. For example, the generation AI converts what the user says into text using speech recognition technology. The generation AI can also remove noise from the speech to produce a highly accurate transcription. Furthermore, the generation AI can analyze the intent of the user's speech and add supplementary information to clarify ambiguous expressions. Step 4: The script creation unit creates a script based on the user's pronunciation. For example, the generation AI can learn the user's pronunciation characteristics and create a script by selecting words that are easy to pronounce. The generation AI can also analyze the user's past speech data to select natural expressions. Furthermore, the generation AI can create scripts that suit different situations. Step 5: The training unit provides training to improve the user's pronunciation. For example, the generation AI analyzes the user's pronunciation and identifies areas that need improvement. The generation AI can also suggest specific pronunciation exercises to the user and provide training. Furthermore, the generation AI can provide a training program that corresponds to the user's emotional state.

[0048] (Example 2) The articulation support system according to an embodiment of the present invention uses a generation AI to learn the user's voice, read aloud in that tone of voice, transcribe text specifically for that voice, create manuscripts that match the user's articulation, and provide training to improve articulation. As a result, the articulation support system can support people with poor articulation so that they can communicate smoothly in their daily lives.

[0049] The articulation support system according to the embodiment includes a vocal tone learning unit, a reading unit, a transcription unit, a script creation unit, and a training unit. The vocal tone learning unit allows a generation AI to learn the user's voice. For example, the generation AI collects the user's voice data and analyzes and learns the voice characteristics. The generation AI can also use voice synthesis technology to imitate the user's vocal tone. The generation AI can also learn the frequency characteristics and sound quality of the user's voice. The reading unit recites text using the vocal tone learned by the vocal tone learning unit. For example, the generation AI recites text entered by the user in the user's vocal tone. The generation AI can also adjust the intonation and rhythm of the text to read it more naturally. The generation AI can also change the vocal tone depending on the user's emotional state. The transcription unit transcribes the user's voice in real time. For example, the generation AI converts what the user says into text using speech recognition technology. The generation AI can also remove noise from the voice to perform highly accurate transcription. The generation AI can also analyze the intent of the user's speech and add supplementary information to clarify ambiguous expressions. The manuscript creation unit creates a manuscript based on the user's pronunciation. For example, the generation AI can learn the user's pronunciation characteristics and select easy-to-pronounce words to create a manuscript. The generation AI can also analyze the user's past speech data to select natural expressions. The generation AI can also create manuscripts suitable for different situations. The training unit provides training to improve the user's pronunciation. For example, the generation AI can analyze the user's pronunciation and identify areas that need improvement. The generation AI can also suggest specific pronunciation exercises to the user and provide training. The generation AI can also provide a training program tailored to the user's emotional state. As a result, the pronunciation support system according to the embodiment can support people with poor pronunciation in communicating smoothly in everyday life. For example, a user can give a presentation by reading aloud in their own voice or record the contents of a meeting using a dedicated transcription tool.It is also expected that users' speech will improve through the creation of manuscripts suited to their pronunciation and speech improvement training.

[0050] The reading unit can estimate the user's emotional state in real time and recite text in a tone of voice that corresponds to that emotional state. For example, the generation AI analyzes the user's facial expressions and vocal tone to estimate the emotional state in real time. For example, if the user is smiling when speaking, the generation AI detects positive emotions and recites the text in a bright tone of voice. The reading unit can also analyze the intonation and rhythm of the user's voice and select a tone of voice that corresponds to the user's emotional state. For example, if the user is nervous, the generation AI will recite the text in a calm tone of voice. The reading unit can also adjust the intonation and rhythm of the text based on the user's emotional state. This makes it possible to read in a tone of voice that corresponds to the user's emotions.

[0051] The reading unit can analyze the user's speech data and reproduce the most natural intonation and rhythm. For example, the generation AI of the reading unit collects the user's past speech data and uses speech analysis technology to extract intonation and rhythm patterns. For example, it learns the user's frequently used phrases and expressions. The reading unit can also develop an algorithm that reproduces natural intonation and rhythm based on the user's speech data. For example, it can learn the characteristics of the user's speaking style and recite text based on that. The reading unit can also adjust the intonation and rhythm of the text based on the user's speech data. This allows the user's natural speaking style to be reproduced.

[0052] The reading unit can learn the characteristics of the user's voice and enable reading in different languages ​​and dialects. For example, the reading unit's generation AI learns the characteristics of the user's voice and reproduces the pronunciation and intonation in different languages. For example, reading in multiple languages ​​such as English, Japanese, and French is possible. The reading unit can also read in different dialects based on the characteristics of the user's voice. For example, reading in dialects such as Kansai dialect or Tohoku dialect is possible. The reading unit can also adjust the pronunciation in different languages ​​and dialects based on the characteristics of the user's voice. This makes it possible to read in different languages ​​and dialects.

[0053] The transcription unit can estimate the user's emotions and automatically add emojis and emotional expressions according to the emotions. In the transcription unit, for example, the generation AI analyzes the user's voice and estimates their emotional state. For example, if the user is laughing, a smiling emoji is added to the text. The transcription unit can also analyze the user's tone of voice and facial expressions and add emotional expressions according to the emotional state to the text. For example, if the user is surprised, a surprised emotional expression is added to the text. The transcription unit can also adjust the content of the text based on the user's emotional state. This allows emojis and emotional expressions to be added according to the emotions.

[0054] The transcription unit can analyze the intent of the user's utterance and add supplementary information to clarify ambiguous expressions. For example, the generation AI in the transcription unit analyzes the user's utterance and identifies ambiguous expressions. For example, it replaces demonstratives such as "that" with specific nouns. The transcription unit can also analyze the intent of the user's utterance and add supplementary information. For example, it replaces ambiguous expressions such as "that place" with specific place names. The transcription unit can also analyze the context of the user's utterance and add appropriate supplementary information. This makes it possible to add supplementary information to clarify ambiguous expressions.

[0055] The transcription unit can analyze the background sounds of the user's speech and remove noise to produce highly accurate transcriptions. For example, the generation AI in the transcription unit analyzes the background sounds of the user's speech and identifies the noise. For example, it detects the sound of wind or cars. The transcription unit can also remove background sounds using a noise removal algorithm. For example, it can filter environmental noise to make the audio clearer. The transcription unit can also remove noise based on the background sounds of the user's speech and produce highly accurate transcriptions. This makes it possible to remove noise and produce highly accurate transcriptions.

[0056] The manuscript creation unit can estimate the user's emotions and create a manuscript by selecting easy-to-say words that correspond to the emotions. In the manuscript creation unit, for example, the generation AI estimates the user's emotional state in real time and creates a manuscript by selecting easy-to-say words that correspond to the emotions. For example, if the user is nervous, simple words are selected. The manuscript creation unit can also adjust the content of the manuscript based on the user's emotional state using the generation AI. For example, if the user is relaxed, more complex words are selected. The manuscript creation unit can also adjust the structure of the manuscript based on the user's emotional state using the generation AI. This allows the manuscript to be created by selecting easy-to-say words that correspond to the emotions.

[0057] The manuscript creation department can analyze the user's speech data and select the most natural expressions to create the manuscript. For example, the generation AI in the manuscript creation department collects the user's past speech data and develops an algorithm to select natural expressions. For example, it learns the phrases and expressions that users frequently use. The manuscript creation department can also create manuscripts by having the generation AI select natural expressions based on the user's speech data. For example, it can create manuscripts based on phrases used in everyday conversation. The manuscript creation department can also adjust the content of the manuscript based on the user's speech data. This allows the generation AI to create manuscripts by selecting natural expressions for the user.

[0058] The manuscript creation unit can learn the user's enunciation characteristics and create manuscripts appropriate for different situations. For example, the manuscript creation unit will develop a system in which a generation AI learns the user's enunciation characteristics and creates manuscripts appropriate for different situations. For example, it will create manuscripts appropriate for situations such as presentations and meetings. The manuscript creation unit can also have the generation AI select words appropriate for the situation based on the user's enunciation characteristics. For example, formal words will be selected in business situations. The manuscript creation unit can also have the generation AI adjust the structure of the manuscript based on the user's enunciation characteristics. This makes it possible to create manuscripts appropriate for different situations.

[0059] The training unit can estimate the user's emotions and provide a training program that corresponds to the emotions. For example, the generation AI in the training unit estimates the user's emotional state in real time and provides a training program that corresponds to the emotions. For example, if the user is tense, it provides training to help them relax. The training unit can also adjust the content of the training based on the user's emotional state. For example, if the user is tired, it provides lighter training. The training unit can also adjust the progress of the training based on the user's emotional state. This makes it possible to provide a training program that corresponds to the emotions.

[0060] The training department can analyze the user's pronunciation data and create an individually customized training menu. For example, the training department develops a system in which a generation AI analyzes the user's pronunciation data and creates an individually customized training menu. For example, if there is a problem with a particular pronunciation, the training department can focus on practicing that pronunciation. The training department can also adjust the training menu based on the user's pronunciation data using the generation AI. For example, the training content can be changed according to the user's progress. The training department can also adjust the difficulty of the training based on the user's pronunciation data using the generation AI. This makes it possible to create an individually customized training menu.

[0061] The training department can monitor the user's training progress in real time and provide appropriate feedback. For example, the training department develops a system in which the generation AI monitors the user's training progress in real time and provides appropriate feedback. For example, if an improvement in pronunciation is observed, praising feedback is provided. The training department can also adjust the content of the feedback based on the user's training progress using the generation AI. For example, specific advice is provided for areas in which the user is weak. The training department can also adjust the progress of training based on the user's training progress using the generation AI. This allows training progress to be monitored in real time and appropriate feedback to be provided.

[0062] The training department can develop games and apps to help users improve their pronunciation. For example, the training department can develop games that use the generation AI to help users improve their pronunciation. For example, it can provide mini-games that allow users to practice pronunciation in a fun way. The training department can also develop apps that use the generation AI to help users improve their pronunciation. For example, it can provide mobile apps and desktop apps. The training department can also adjust the content of games and apps that use the generation AI to help users improve their pronunciation. This makes it possible to develop games and apps that help users improve their pronunciation.

[0063] The training department can provide group training sessions to help users improve their pronunciation. For example, the training department develops a system in which the generation AI provides group training sessions to help users improve their pronunciation. For example, it provides online sessions in which multiple users can participate simultaneously. The training department can also provide face-to-face sessions in which the generation AI helps users improve their pronunciation. For example, it can conduct group pronunciation practice. The training department can also adjust the content of the session in which the generation AI helps users improve their pronunciation. In this way, it is possible to provide group training sessions to help improve pronunciation.

[0064] The training unit uses the emotion estimation function to provide a training program that corresponds to the user's emotions, thereby increasing motivation. For example, the generation AI in the training unit estimates the user's emotional state in real time and provides a training program that corresponds to that emotion. For example, if the user is tired, it provides a lighter training program. The training unit can also adjust the content of the training based on the user's emotional state. For example, if the user is losing motivation, it provides training that will increase motivation. The training unit can also adjust the progress of the training based on the user's emotional state. This allows the training unit to provide a training program that corresponds to the user's emotions and increase motivation.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The articulation support system can also include a health management unit that monitors the user's vocal health. For example, the generative AI can analyze changes in the user's voice and detect vocal fatigue or abnormalities. The health management unit can also provide the user with advice on maintaining vocal health. For example, it can recommend vocal rest or suggest appropriate vocal exercises. The health management unit can also record the user's vocal health and analyze long-term trends. This allows the user to improve their articulation while maintaining vocal health.

[0067] The articulation support system can further include a music providing unit that estimates the user's emotions and provides music that corresponds to the emotions. For example, the generation AI estimates the user's emotional state in real time and selects music that corresponds to that emotion. For example, if the user wants to relax, it provides calm music. The music providing unit can also adjust the tempo and genre of the music based on the user's emotional state. For example, if the user wants to concentrate, it provides up-tempo music. The music providing unit can also customize a music playlist based on the user's emotional state. This makes it possible to provide music that corresponds to the user's emotions.

[0068] The articulation support system can further include a feedback unit that analyzes the content of a user's speech and provides appropriate feedback. For example, the generation AI analyzes the content of a user's speech and identifies areas for improvement in pronunciation. The feedback unit can also provide the user with specific pronunciation practice advice. For example, it can show how to pronounce a specific sound. The feedback unit can also evaluate the user's pronunciation progress based on the content of the user's speech. This allows the user to improve their articulation while receiving specific feedback.

[0069] The articulation support system can further include a relaxation unit that estimates the user's emotions and provides relaxation methods according to those emotions. For example, the generation AI can estimate the user's emotional state in real time and suggest relaxation methods according to those emotions. For example, if the user is feeling stressed, it can recommend deep breathing or meditation. The relaxation unit can also adjust the content of relaxation based on the user's emotional state. For example, if the user is nervous, it can provide relaxing music. The relaxation unit can also adjust the progress of relaxation based on the user's emotional state. This makes it possible to provide relaxation methods according to the user's emotions.

[0070] The articulation support system can further include a visualization unit that analyzes the user's utterance data and visualizes pronunciation patterns. For example, the generation AI analyzes the user's utterance data and displays pronunciation characteristics in graphs or charts. The visualization unit can also visually show the user areas for improvement in pronunciation. For example, if the pronunciation of a particular sound is unclear, that part can be highlighted. The visualization unit can also visually track the user's pronunciation progress. This allows the user to train while visually checking the progress of their pronunciation.

[0071] The articulation support system can further include a communication support unit that estimates the user's emotions and suggests a communication method according to the emotion. For example, the generation AI estimates the user's emotional state in real time and suggests a communication method according to that emotion. For example, if the user is nervous, it would recommend speaking in a relaxed tone. The communication support unit can also suggest appropriate language and expressions based on the user's emotional state. For example, if the user is angry, it would recommend using calm language. The communication support unit can also adjust the progress of communication based on the user's emotional state. This makes it possible to suggest a communication method according to the user's emotions.

[0072] The articulation support system can further include a gamification unit that analyzes the user's utterance data and turns pronunciation practice into a game. For example, a generation AI analyzes the user's utterance data and provides pronunciation practice in the form of a game. The gamification unit can also provide mini-games that allow the user to practice pronunciation in a fun way. For example, a game can be provided in which points can be earned by correctly pronouncing specific sounds. The gamification unit can also evaluate the user's pronunciation progress within the game and provide rewards. This allows the user to practice pronunciation in a fun way.

[0073] The articulation support system can further include a feedback reinforcement unit that estimates the user's emotions and provides feedback according to those emotions. For example, the generation AI estimates the user's emotional state in real time and provides feedback according to those emotions. For example, if the user is feeling down, it can provide words of encouragement. The feedback reinforcement unit can also adjust the content of the feedback based on the user's emotional state. For example, if the user is feeling confident, it can suggest further challenges. The feedback reinforcement unit can also adjust the timing of the feedback based on the user's emotional state. This makes it possible to provide feedback according to the user's emotions.

[0074] The articulation support system can further include a teaching material providing unit that analyzes the user's utterance data and provides customized teaching materials to support pronunciation improvement. For example, the generation AI analyzes the user's utterance data and selects teaching materials necessary for pronunciation improvement. The teaching material providing unit can also provide pronunciation practice materials that are individually customized for the user. For example, it can provide teaching materials that focus on practicing the pronunciation of specific sounds. The teaching material providing unit can also adjust the content of the teaching materials based on the user's pronunciation progress. This allows the user to practice pronunciation using teaching materials that suit them.

[0075] The articulation support system can further include a motivation unit that estimates the user's emotions and provides a motivation-boosting message according to the emotion. For example, the generation AI estimates the user's emotional state in real time and provides a motivation-boosting message according to the emotion. For example, if the user is tired, it provides an encouraging message. The motivation unit can also adjust the content of the message based on the user's emotional state. For example, if the user is unmotivated, it provides a positive message. The motivation unit can also adjust the timing of the message based on the user's emotional state. This makes it possible to provide a motivation-boosting message according to the user's emotions.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: In the voice tone learning section, the generation AI learns the user's voice. For example, the generation AI collects the user's voice data and analyzes and learns the voice characteristics. The generation AI can also use voice synthesis technology to imitate the user's voice tone. Furthermore, the generation AI can learn the frequency characteristics and sound quality of the user's voice. Step 2: The reading unit reads the text in the tone of voice learned by the tone of voice learning unit. For example, the generation AI reads the text entered by the user in the user's tone of voice. The generation AI can also adjust the intonation and rhythm of the text to read it more naturally. Furthermore, the generation AI can change the tone of voice depending on the user's emotional state. Step 3: The transcription part transcribes the user's speech in real time. For example, the generation AI converts what the user says into text using speech recognition technology. The generation AI can also remove noise from the speech to produce a highly accurate transcription. Furthermore, the generation AI can analyze the intent of the user's speech and add supplementary information to clarify ambiguous expressions. Step 4: The script creation unit creates a script based on the user's pronunciation. For example, the generation AI can learn the user's pronunciation characteristics and create a script by selecting words that are easy to pronounce. The generation AI can also analyze the user's past speech data to select natural expressions. Furthermore, the generation AI can create scripts that suit different situations. Step 5: The training unit provides training to improve the user's pronunciation. For example, the generation AI analyzes the user's pronunciation and identifies areas that need improvement. The generation AI can also suggest specific pronunciation exercises to the user and provide training. Furthermore, the generation AI can provide a training program that corresponds to the user's emotional state.

[0078] 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.

[0079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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. Using generative AI, a voice tone learning unit that allows the generation AI to learn the user's voice; a reading unit that reads a text aloud in the tone of voice learned by the tone of voice learning unit; a transcription unit that transcribes the user's voice in real time; a manuscript creation unit that creates a manuscript based on the user's pronunciation; a training unit that provides training to improve the user's pronunciation. A system characterized by:

2. The reading section The emotional state of the user is estimated in real time, and the text is read aloud in the tone of voice corresponding to the emotional state.

2. The system of claim 1.

3. The transcription unit Inferring the user's emotions and automatically adding emojis and emotional expressions according to the emotions.

2. The system of claim 1.

4. The manuscript creation unit The user's feelings are estimated, and the easy-to-say words corresponding to the feelings are selected to create the manuscript.

2. The system of claim 1.

5. The training section Estimating the user's emotions and providing a training program according to the emotions 2. The system of claim 1.

6. The reading section Analyze the user's speech data and reproduce the most natural intonation and rhythm 2. The system of claim 1.

7. The transcription unit Analyze the intent of the user's statement and add supplementary information to clarify ambiguous expressions 2. The system of claim 1.

8. The manuscript creation unit Analyze the user's speech data and select the most natural expressions to create the manuscript.

2. The system of claim 1.

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