System

Abstract

An object of a system according to an embodiment is to learn a voice of a user and generate a voice that is easily transmitted to a partner even by a user with poor articulation.SOLUTION: A system according to an embodiment includes a voice learning unit, a document input unit, a voice generation unit, and a reproduction control unit. The voice learning unit learns the voice of the user. The document input unit receives text input by a user. The voice generation unit generates a voice of the user from the text received by the document input unit. The reproduction control unit reproduces the sound generated by the sound generation unit at an arbitrary timing.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 technology has had the problem that users with poor pronunciation have difficulty communicating with others on the phone.

[0005] The system according to the embodiment aims to learn the user's voice and generate speech that is easy for the other person to understand, even for users with poor pronunciation. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice learning unit, a manuscript input unit, a voice generation unit, and a playback control unit. The voice learning unit learns the user's voice. The manuscript input unit receives text input by the user. The voice generation unit generates voice in the user's voice for the text received by the manuscript input unit. The playback control unit plays back the voice generated by the voice generation unit at any timing. [Effects of the Invention]

[0007] The system according to the embodiment learns the user's voice and can generate speech that is easy for the other person to understand, even for users with poor pronunciation. [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 Read-Aloud in Your Own Voice option according to an embodiment of the present invention is a system that learns the user's voice and plays the audio at any timing. This solves the problem of poor pronunciation leading to frequent repeats or paraphrasing.

[0029] The read-aloud option according to the embodiment includes a voice learning unit, a manuscript input unit, a voice generation unit, and a playback control unit. The voice learning unit learns the user's voice. For example, the user reads several sample sentences and inputs the resulting voice data into the generation AI. The voice learning unit then analyzes the voice data and the generation AI learns the characteristics of the user's voice. For example, the generation AI analyzes the voice frequency, volume, and voice quality. The manuscript input unit receives text entered by the user. For example, the user inputs a manuscript they want to read aloud during a phone call in text format. The manuscript input unit can also automatically convert what the user says into text using voice recognition technology. For example, the voice recognition technology can convert what the user says into text in real time. The voice generation unit generates voice in the user's voice from the text received by the manuscript input unit. For example, the generation AI generates voice using the learned user voice. The generation AI can also add appropriate emotional expressions depending on the content of the manuscript, achieving an emotionally rich reading. For example, the generation AI analyzes the content of the manuscript and adds emotional expressions. The playback control unit plays the audio generated by the audio generation unit at any timing. For example, after a user says, "I'm going to read the manuscript now," pressing a button plays audio in which the AI ​​reads the manuscript. The playback control unit can also monitor the user's emotional state in real time and suggest audio playback at appropriate times. For example, facial recognition technology can be used to analyze facial expressions and estimate the user's emotional state. This allows the reading-in-your-own option according to the embodiment to solve the problem of poor pronunciation, which often leads to repeating or paraphrasing. For example, this can allow users to accurately convey important information during business calls and avoid misunderstandings when talking with friends and family.

[0030] The voice learning unit can learn not only the characteristics of the user's voice, but also the speaking habits and rhythm. For example, the voice learning unit collects phrases and expressions that the user uses on a daily basis and learns the speaking habits and rhythm. For example, the habit of emphasizing certain words can be reflected in the generation AI. The voice learning unit can also analyze the rhythm of the user's speaking and reflect this in the generation AI. For example, if the user speaks quickly, the voice learning unit can learn this rhythm and reflect it in the voice generation. In this way, by learning the user's speaking habits and rhythm, more natural voice generation becomes possible.

[0031] The voice learning unit can learn changes in the user's voice and generate voices that suit the situation. For example, the voice learning unit collects voice data from when the user has a cold or is tired, and learns the changes in the user's voice. For example, a nasal or hoarse voice can be reflected in the generation AI. The voice learning unit can also analyze changes in the user's voice and reflect these in the generation AI. For example, it can learn changes in the user's voice when they are nervous and reflect these in the voice generation. In this way, by learning changes in the user's voice, it becomes possible to generate voices that suit the situation.

[0032] The speech generation unit can understand the context of the manuscript and generate speech that emphasizes important parts. For example, the speech generation unit analyzes the context of the manuscript and develops an algorithm that emphasizes important parts. For example, it generates speech that emphasizes keywords and phrases. The speech generation unit can also use the generation AI to understand the context of the manuscript and emphasize important parts. For example, the generation AI analyzes the contents of the manuscript and generates speech that emphasizes important parts. This makes it possible to understand the context of the manuscript and emphasize important parts, thereby enabling more effective communication of information.

[0033] The audio generation unit can automatically add appropriate background sounds and sound effects based on the content of the manuscript. The audio generation unit, for example, develops a system that analyzes the content of the manuscript and automatically generates appropriate background sounds and sound effects. For example, natural sounds and music are added. The audio generation unit can also add background sounds and sound effects based on the content of the manuscript using a generation AI. For example, the generation AI analyzes the content of the manuscript and adds appropriate background sounds and sound effects. This makes it possible to generate audio with a more realistic feel by adding background sounds and sound effects based on the content of the manuscript.

[0034] The manuscript input unit can automatically convert what a user says into text using voice recognition technology. The manuscript input unit develops a system that converts what a user says into text in real time using voice recognition technology, for example. For example, it automatically transcribes conversations. The manuscript input unit can also use a generation AI to convert what a user says into text using voice recognition technology. For example, the generation AI uses a voice recognition algorithm to convert what a user says into text. This makes it easier to input manuscripts by automatically converting what a user says into text using voice recognition technology.

[0035] The manuscript input unit can visually display the contents of the manuscript and provide an interface that is easy for the user to check. The manuscript input unit, for example, develops an interface that visually displays the contents of the manuscript. For example, it displays text in a large font. The manuscript input unit can also provide an interface that allows the generation AI to visually display the contents of the manuscript. For example, the generation AI analyzes the contents of the manuscript and displays them in a format that is easy for the user to check. In this way, visually displaying the contents of the manuscript makes it easier for the user to check.

[0036] The playback control unit can automatically adjust the timing of audio playback to match the flow of the user's conversation. For example, the playback control unit analyzes the flow of the user's conversation and develops an algorithm that plays audio at the appropriate timing. For example, it plays audio immediately after the user finishes speaking. The playback control unit can also have the generation AI analyze the flow of the user's conversation and automatically adjust the timing of audio playback. For example, the generation AI analyzes the topic of the conversation and the timing of speaker turns and adjusts the timing of audio playback. This automatically adjusts the timing of audio playback to match the flow of the user's conversation, enabling more natural conversations.

[0037] The playback control unit can link the timing of audio playback with the user's calendar or schedule, and automatically play audio before important events or meetings. For example, the playback control unit can analyze the user's calendar or schedule and develop a system that automatically plays audio before important events or meetings. For example, a reminder audio can be played five minutes before the start of a meeting. The playback control unit can also enable the generation AI to analyze the user's calendar or schedule and adjust the timing of audio playback. For example, the generation AI adjusts the timing of audio playback based on calendar event information. In this way, by linking the timing of audio playback with the calendar or schedule, automatic playback before important events or meetings becomes possible.

[0038] The playback control unit can link the timing of audio playback with the user's location information and automatically play audio at a specific location. The playback control unit, for example, develops a system that analyzes the user's location information and automatically plays audio when the user arrives at a specific location. For example, it plays audio to announce the start of work when the user arrives at the office. The playback control unit can also adjust the timing of audio playback by having the generation AI analyze the user's location information. For example, the generation AI adjusts the timing of audio playback based on GPS data. This makes it possible to automatically play audio at a specific location by linking the timing of audio playback with location information.

[0039] The speech generation unit can analyze the user's past conversation data and learn the optimal speaking speed. For example, the speech generation unit collects the user's past conversation data and develops a system that learns the optimal speaking speed based on that data. For example, it analyzes the tempo of past conversations. The speech generation unit can also have the generation AI analyze the user's past conversation data and learn the optimal speaking speed. For example, the generation AI adjusts the speaking speed based on the past conversation data. This allows for more effective speech generation by analyzing the user's past conversation data and learning the optimal speaking speed.

[0040] The voice generation unit can adjust not only the speaking speed but also the volume and pitch simultaneously. For example, the voice generation unit will develop a system that simultaneously adjusts the volume and pitch according to the user's speaking speed. For example, lowering the volume when speaking slowly. The voice generation unit can also adjust the volume and pitch according to the speaking speed of the generation AI. For example, the generation AI will develop an algorithm that adjusts the volume and pitch according to the speaking speed. This will enable more natural voice generation by simultaneously adjusting not only the speaking speed but also the volume and pitch.

[0041] The speech generation unit can adjust speaking speed in conjunction with the user's breathing pattern, enabling natural conversation. For example, the speech generation unit will develop a system that monitors the user's breathing pattern and adjusts speaking speed based on that data. For example, speaking more slowly when breathing is shallow. The speech generation unit can also adjust speaking speed by having the generation AI analyze the user's breathing pattern. For example, the generation AI uses a breathing sensor to analyze the user's breathing pattern and adjust speaking speed. This allows for more natural conversation by adjusting speaking speed in conjunction with breathing patterns.

[0042] The voice generation unit can link the adjustment of speaking speed to the user's heart rate and stress level. For example, the voice generation unit will develop a system that monitors the user's heart rate and adjusts speaking speed based on that data. For example, speaking more slowly when the heart rate is high. The voice generation unit can also adjust speaking speed by having the generation AI analyze the user's heart rate and stress level. For example, the generation AI can analyze the user's heart rate using a heart rate sensor and adjust speaking speed. This makes it possible to link the adjustment of speaking speed to heart rate and stress level, thereby generating more natural voice.

[0043] The playback control unit can analyze the history data of the calling app and optimize voice generation based on the content of past conversations. The playback control unit, for example, collects history data of the calling app and develops a system that optimizes voice generation based on that data. For example, it analyzes the content of past conversations. The playback control unit can also enable a generation AI to analyze the history data of the calling app and optimize voice generation. For example, the generation AI adjusts voice generation based on data of past conversations. This enables more effective voice generation by analyzing the history data of the calling app and optimizing voice generation based on the content of past conversations.

[0044] The playback control unit can work in conjunction with the notification function of a calling app to automatically read out important messages aloud. The playback control unit, for example, can work in conjunction with the notification function of a calling app to develop a system that automatically reads out important messages aloud. For example, it can notify emergency messages aloud. The playback control unit can also have a generation AI work in conjunction with the notification function of a calling app to read out important messages aloud. For example, the generation AI uses the notification function to read out important messages aloud. This makes it possible to quickly convey important information by working in conjunction with the notification function of a calling app to automatically read out important messages aloud.

[0045] The playback control unit can work in conjunction with a calling app to automatically generate subtitles during video calls to visually supplement information. The playback control unit can, for example, work in conjunction with a calling app to develop a system that automatically generates subtitles in real time during video calls. For example, it can transcribe conversations using speech recognition technology. The playback control unit can also have a generation AI work in conjunction with the calling app to automatically generate subtitles during video calls. For example, the generation AI can transcribe conversations using a speech recognition algorithm and generate subtitles. This enables more effective communication by automatically generating subtitles during video calls to visually supplement information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] In addition to learning the user's voice, the system can monitor changes in the user's voice in real time and generate speech appropriate to the situation. For example, it can collect voice data when the user has a cold or is tired and learn the changes in the user's voice. By learning the changes in the user's voice, it is possible to generate speech appropriate to the situation. It can also learn the changes in the user's voice when they are nervous and reflect this in the speech generation. By learning the changes in the user's voice, it is possible to generate more natural speech.

[0048] In addition to learning the user's speaking habits and rhythm, it can also analyze the user's past conversation data to learn the optimal speaking speed and volume. For example, it can collect phrases and expressions that the user uses daily and learn their speaking habits and rhythm. It can also analyze the tempo of past conversations to learn the optimal speaking speed. In this way, by learning the user's speaking habits and rhythm, it becomes possible to generate more natural voice.

[0049] Not only can it add appropriate emotional expressions depending on the content of the manuscript, but it can also understand the context of the manuscript and generate speech that emphasizes important parts. For example, it can analyze the content of the manuscript and generate speech that emphasizes keywords and phrases. The generation AI can also understand the context of the manuscript and emphasize important parts. This allows for more effective communication by understanding the context of the manuscript and emphasizing important parts.

[0050] The manuscript input unit not only automatically converts what the user has said into text using speech recognition technology, but also converts the text into text that reflects the user's speaking habits and rhythm. For example, the text can be converted into text that reflects the user's habit of emphasizing certain words. It can also be converted into text that reflects the user's speaking rhythm. This makes it possible to convert the text into text that reflects the user's speaking habits and rhythm, resulting in more natural conversion.

[0051] The playback control unit not only monitors the user's emotional state in real time and suggests audio playback at the appropriate time, but also links with the user's calendar and schedule to automatically play audio before important events or meetings. For example, a reminder audio can be played five minutes before the start of a meeting. The generation AI can also analyze the user's calendar and schedule and adjust the timing of audio playback. This makes it possible to automatically play audio before important events or meetings by linking the timing of audio playback with the user's calendar and schedule.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The voice learning unit learns the user's voice. For example, the user reads some sample sentences and inputs the resulting voice data into the generation AI. The generation AI then analyzes this voice data and learns the characteristics of the user's voice. Specifically, it analyzes the voice frequency, volume, and voice quality. Step 2: The manuscript input unit receives text entered by the user. For example, the user inputs the manuscript they want to read out loud during a phone call in text format. It is also possible to automatically convert what the user says into text using voice recognition technology. Specifically, voice recognition technology is used to convert what the user says into text in real time. Step 3: The speech generation unit generates speech in the user's voice from the text received by the manuscript input unit. For example, the generation AI generates speech using the trained user's voice. The generation AI can also add appropriate emotional expressions depending on the content of the manuscript, achieving an emotionally rich reading. Specifically, the generation AI analyzes the content of the manuscript and adds emotional expressions. Step 4: The playback control unit plays the audio generated by the audio generation unit at any time. For example, after the user says, "I will now read the manuscript," pressing a button will play the audio in which the AI ​​reads the manuscript. The playback control unit can also monitor the user's emotional state in real time and suggest audio playback at an appropriate time. Specifically, it uses facial recognition technology to analyze facial expressions and estimate the user's emotional state.

[0054] (Example 2) The Read-Aloud in Your Own Voice option according to an embodiment of the present invention is a system that learns the user's voice and plays the audio at any timing. This solves the problem of poor pronunciation leading to frequent repeats or paraphrasing.

[0055] The read-aloud option according to the embodiment includes a voice learning unit, a manuscript input unit, a voice generation unit, and a playback control unit. The voice learning unit learns the user's voice. For example, the user reads several sample sentences and inputs the resulting voice data into the generation AI. The voice learning unit then analyzes the voice data and the generation AI learns the characteristics of the user's voice. For example, the generation AI analyzes the voice frequency, volume, and voice quality. The manuscript input unit receives text entered by the user. For example, the user inputs a manuscript they want to read aloud during a phone call in text format. The manuscript input unit can also automatically convert what the user says into text using voice recognition technology. For example, the voice recognition technology can convert what the user says into text in real time. The voice generation unit generates voice in the user's voice from the text received by the manuscript input unit. For example, the generation AI generates voice using the learned user voice. The generation AI can also add appropriate emotional expressions depending on the content of the manuscript, achieving an emotionally rich reading. For example, the generation AI analyzes the content of the manuscript and adds emotional expressions. The playback control unit plays the audio generated by the audio generation unit at any timing. For example, after a user says, "I'm going to read the manuscript now," pressing a button plays audio in which the AI ​​reads the manuscript. The playback control unit can also monitor the user's emotional state in real time and suggest audio playback at appropriate times. For example, facial recognition technology can be used to analyze facial expressions and estimate the user's emotional state. This allows the reading-in-your-own option according to the embodiment to solve the problem of poor pronunciation, which often leads to repeating or paraphrasing. For example, this can allow users to accurately convey important information during business calls and avoid misunderstandings when talking with friends and family.

[0056] The voice learning unit can estimate the user's emotional state and learn the tone and intonation of voice that correspond to that emotional state. For example, when the user reads a sample sentence, the voice learning unit uses facial recognition technology to analyze the user's facial expression and estimate the emotional state. For example, the voice learning unit learns the tone and intonation of voice when smiling and reflects this in the generation AI. The voice learning unit can also use voice analysis technology to analyze the user's tone and intonation of voice and learn voice characteristics that correspond to the user's emotional state. For example, the generation AI can learn the tone and intonation of voice when the user is angry and reflect this in the voice generation. In this way, by learning the tone and intonation of voice that correspond to the user's emotional state, more natural voice generation is possible.

[0057] The voice learning unit can learn not only the characteristics of the user's voice, but also the speaking habits and rhythm. For example, the voice learning unit collects phrases and expressions that the user uses on a daily basis and learns the speaking habits and rhythm. For example, the habit of emphasizing certain words can be reflected in the generation AI. The voice learning unit can also analyze the rhythm of the user's speaking and reflect this in the generation AI. For example, if the user speaks quickly, the voice learning unit can learn this rhythm and reflect it in the voice generation. In this way, by learning the user's speaking habits and rhythm, more natural voice generation becomes possible.

[0058] The voice learning unit can learn changes in the user's voice and generate voices that suit the situation. For example, the voice learning unit collects voice data from when the user has a cold or is tired, and learns the changes in the user's voice. For example, a nasal or hoarse voice can be reflected in the generation AI. The voice learning unit can also analyze changes in the user's voice and reflect these in the generation AI. For example, it can learn changes in the user's voice when they are nervous and reflect these in the voice generation. In this way, by learning changes in the user's voice, it becomes possible to generate voices that suit the situation.

[0059] The voice generation unit can add appropriate emotional expressions according to the content of the manuscript, achieving an emotionally rich reading. The voice generation unit, for example, analyzes the content of the manuscript and develops an algorithm to add emotional expressions. For example, it generates voice that expresses emotions such as joy or sadness. The voice generation unit can also use a generation AI to add emotional expressions based on the content of the manuscript. For example, the generation AI analyzes the content of the manuscript and adds emotional expressions based on an emotional score. This allows for an emotionally rich reading by adding emotional expressions according to the content of the manuscript.

[0060] The speech generation unit can understand the context of the manuscript and generate speech that emphasizes important parts. For example, the speech generation unit analyzes the context of the manuscript and develops an algorithm that emphasizes important parts. For example, it generates speech that emphasizes keywords and phrases. The speech generation unit can also use the generation AI to understand the context of the manuscript and emphasize important parts. For example, the generation AI analyzes the contents of the manuscript and generates speech that emphasizes important parts. This makes it possible to understand the context of the manuscript and emphasize important parts, thereby enabling more effective communication of information.

[0061] The audio generation unit can automatically add appropriate background sounds and sound effects based on the content of the manuscript. The audio generation unit, for example, develops a system that analyzes the content of the manuscript and automatically generates appropriate background sounds and sound effects. For example, natural sounds and music are added. The audio generation unit can also add background sounds and sound effects based on the content of the manuscript using a generation AI. For example, the generation AI analyzes the content of the manuscript and adds appropriate background sounds and sound effects. This makes it possible to generate audio with a more realistic feel by adding background sounds and sound effects based on the content of the manuscript.

[0062] The manuscript input unit can automatically convert what a user says into text using voice recognition technology. The manuscript input unit develops a system that converts what a user says into text in real time using voice recognition technology, for example. For example, it automatically transcribes conversations. The manuscript input unit can also use a generation AI to convert what a user says into text using voice recognition technology. For example, the generation AI uses a voice recognition algorithm to convert what a user says into text. This makes it easier to input manuscripts by automatically converting what a user says into text using voice recognition technology.

[0063] The manuscript input unit can visually display the contents of the manuscript and provide an interface that is easy for the user to check. The manuscript input unit, for example, develops an interface that visually displays the contents of the manuscript. For example, it displays text in a large font. The manuscript input unit can also provide an interface that allows the generation AI to visually display the contents of the manuscript. For example, the generation AI analyzes the contents of the manuscript and displays them in a format that is easy for the user to check. In this way, visually displaying the contents of the manuscript makes it easier for the user to check.

[0064] The manuscript input unit can use the emotion estimation function to analyze the user's emotions regarding the contents of the manuscript and generate speech in accordance with the emotions. The manuscript input unit, for example, uses the emotion estimation function to develop a system that analyzes the user's emotions regarding the contents of the manuscript in real time. For example, it analyzes the user's facial expressions and voice. The manuscript input unit can also have a generation AI that uses the emotion estimation function to analyze the user's emotions and generate speech in accordance with the emotions. For example, the generation AI generates speech based on the emotion score. This allows the generation of more emotional speech by analyzing the user's emotions regarding the contents of the manuscript and generating speech in accordance with the emotions.

[0065] The playback control unit can monitor the user's emotional state in real time and suggest audio playback at the appropriate timing. The playback control unit, for example, develops a system that monitors the user's emotional state in real time. For example, it analyzes facial expressions using facial recognition technology. The playback control unit can also have a generation AI monitor the user's emotional state in real time and suggest audio playback at the appropriate timing. For example, the generation AI suggests the timing of audio playback based on the emotion score. This enables more effective information transmission by monitoring the user's emotional state in real time and suggesting audio playback at the appropriate timing.

[0066] The playback control unit can automatically adjust the timing of audio playback to match the flow of the user's conversation. For example, the playback control unit analyzes the flow of the user's conversation and develops an algorithm that plays audio at the appropriate timing. For example, it plays audio immediately after the user finishes speaking. The playback control unit can also have the generation AI analyze the flow of the user's conversation and automatically adjust the timing of audio playback. For example, the generation AI analyzes the topic of the conversation and the timing of speaker turns and adjusts the timing of audio playback. This automatically adjusts the timing of audio playback to match the flow of the user's conversation, enabling more natural conversations.

[0067] The playback control unit can link the timing of audio playback with the user's calendar or schedule, and automatically play audio before important events or meetings. For example, the playback control unit can analyze the user's calendar or schedule and develop a system that automatically plays audio before important events or meetings. For example, a reminder audio can be played five minutes before the start of a meeting. The playback control unit can also enable the generation AI to analyze the user's calendar or schedule and adjust the timing of audio playback. For example, the generation AI adjusts the timing of audio playback based on calendar event information. In this way, by linking the timing of audio playback with the calendar or schedule, automatic playback before important events or meetings becomes possible.

[0068] The playback control unit can link the timing of audio playback with the user's location information and automatically play audio at a specific location. The playback control unit, for example, develops a system that analyzes the user's location information and automatically plays audio when the user arrives at a specific location. For example, it plays audio to announce the start of work when the user arrives at the office. The playback control unit can also adjust the timing of audio playback by having the generation AI analyze the user's location information. For example, the generation AI adjusts the timing of audio playback based on GPS data. This makes it possible to automatically play audio at a specific location by linking the timing of audio playback with location information.

[0069] The playback control unit can use the emotion estimation function to play audio to help the user relax when they are tense. The playback control unit, for example, uses the emotion estimation function to develop a system that automatically plays audio to help the user relax when they are tense. For example, it plays relaxing music or audio to guide deep breathing. The playback control unit can also use the generation AI to analyze the user's state of tension using the emotion estimation function and play relaxing audio. For example, the generation AI plays relaxing audio based on heart rate fluctuations. In this way, the emotion estimation function can be used to play relaxing audio when the user is tense, thereby reducing the user's stress.

[0070] The speech generation unit can automatically adjust the speaking speed according to the user's emotional state. For example, the speech generation unit will develop a system that automatically adjusts the speaking speed according to the user's emotional state using an emotion estimation function. For example, when the user is nervous, the speech generation unit can also adjust the speaking speed by having the generation AI analyze the user's emotional state. For example, the generation AI adjusts the speaking speed based on the emotion score. This makes it possible to automatically adjust the speaking speed according to the user's emotional state, thereby generating more natural speech.

[0071] The speech generation unit can analyze the user's past conversation data and learn the optimal speaking speed. For example, the speech generation unit collects the user's past conversation data and develops a system that learns the optimal speaking speed based on that data. For example, it analyzes the tempo of past conversations. The speech generation unit can also have the generation AI analyze the user's past conversation data and learn the optimal speaking speed. For example, the generation AI adjusts the speaking speed based on the past conversation data. This allows for more effective speech generation by analyzing the user's past conversation data and learning the optimal speaking speed.

[0072] The voice generation unit can adjust not only the speaking speed but also the volume and pitch simultaneously. For example, the voice generation unit will develop a system that simultaneously adjusts the volume and pitch according to the user's speaking speed. For example, lowering the volume when speaking slowly. The voice generation unit can also adjust the volume and pitch according to the speaking speed of the generation AI. For example, the generation AI will develop an algorithm that adjusts the volume and pitch according to the speaking speed. This will enable more natural voice generation by simultaneously adjusting not only the speaking speed but also the volume and pitch.

[0073] The speech generation unit can adjust speaking speed in conjunction with the user's breathing pattern, enabling natural conversation. For example, the speech generation unit will develop a system that monitors the user's breathing pattern and adjusts speaking speed based on that data. For example, speaking more slowly when breathing is shallow. The speech generation unit can also adjust speaking speed by having the generation AI analyze the user's breathing pattern. For example, the generation AI uses a breathing sensor to analyze the user's breathing pattern and adjust speaking speed. This allows for more natural conversation by adjusting speaking speed in conjunction with breathing patterns.

[0074] The voice generation unit can link the adjustment of speaking speed to the user's heart rate and stress level. For example, the voice generation unit will develop a system that monitors the user's heart rate and adjusts speaking speed based on that data. For example, speaking more slowly when the heart rate is high. The voice generation unit can also adjust speaking speed by having the generation AI analyze the user's heart rate and stress level. For example, the generation AI can analyze the user's heart rate using a heart rate sensor and adjust speaking speed. This makes it possible to link the adjustment of speaking speed to heart rate and stress level, thereby generating more natural voice.

[0075] The speech generation unit can use the emotion estimation function to generate speech at a slower speed when the user is relaxed. For example, a system can be developed in which the speech generation unit uses the emotion estimation function to generate speech at a slower speed when the user is relaxed. For example, speech that sounds like a relaxed state is generated. The speech generation unit can also use the emotion estimation function to have the generation AI analyze the user's state of relaxation and reflect this in the speech generation. For example, the generation AI generates a relaxing speech based on a decrease in heart rate. This makes it possible to generate more natural speech by using the emotion estimation function to generate speech at a slower speed when the user is relaxed.

[0076] The playback control unit can analyze the user's emotional state in real time within the calling app and generate appropriate voice. The playback control unit, for example, develops a system that analyzes the user's emotional state in real time within the calling app. For example, it estimates emotions using voice analysis technology. The playback control unit can also enable a generation AI to analyze the user's emotional state in the calling app and generate appropriate voice. For example, the generation AI adjusts the voice generation based on the emotion score. This allows the user's emotional state in the calling app to be analyzed in real time and appropriate voice generation to enable more effective communication.

[0077] The playback control unit can analyze the history data of the calling app and optimize voice generation based on the content of past conversations. The playback control unit, for example, collects history data of the calling app and develops a system that optimizes voice generation based on that data. For example, it analyzes the content of past conversations. The playback control unit can also enable a generation AI to analyze the history data of the calling app and optimize voice generation. For example, the generation AI adjusts voice generation based on data of past conversations. This enables more effective voice generation by analyzing the history data of the calling app and optimizing voice generation based on the content of past conversations.

[0078] The playback control unit can work in conjunction with the notification function of a calling app to automatically read out important messages aloud. The playback control unit, for example, can work in conjunction with the notification function of a calling app to develop a system that automatically reads out important messages aloud. For example, it can notify emergency messages aloud. The playback control unit can also have a generation AI work in conjunction with the notification function of a calling app to read out important messages aloud. For example, the generation AI uses the notification function to read out important messages aloud. This makes it possible to quickly convey important information by working in conjunction with the notification function of a calling app to automatically read out important messages aloud.

[0079] The playback control unit can work in conjunction with a calling app to automatically generate subtitles during video calls to visually supplement information. The playback control unit can, for example, work in conjunction with a calling app to develop a system that automatically generates subtitles in real time during video calls. For example, it can transcribe conversations using speech recognition technology. The playback control unit can also have a generation AI work in conjunction with the calling app to automatically generate subtitles during video calls. For example, the generation AI can transcribe conversations using a speech recognition algorithm and generate subtitles. This enables more effective communication by automatically generating subtitles during video calls to visually supplement information.

[0080] The playback control unit can use the emotion estimation function to analyze the emotional state of the other party during a call and suggest an appropriate response. The playback control unit, for example, uses the emotion estimation function to develop a system that analyzes the emotional state of the other party during a call in real time. For example, emotions are estimated using voice analysis technology. The playback control unit can also have the generation AI analyze the emotional state of the other party during a call and suggest an appropriate response. For example, the generation AI suggests a response based on an emotion score. This enables more effective communication by analyzing the emotional state of the other party during a call and suggesting an appropriate response.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] In addition to learning the user's voice, the system can monitor changes in the user's voice in real time and generate speech appropriate to the situation. For example, it can collect voice data when the user has a cold or is tired and learn the changes in the user's voice. By learning the changes in the user's voice, it is possible to generate speech appropriate to the situation. It can also learn the changes in the user's voice when they are nervous and reflect this in the speech generation. By learning the changes in the user's voice, it is possible to generate more natural speech.

[0083] Not only the tone and intonation of the voice, but also the speaking speed and volume can be adjusted according to the user's emotional state. For example, if the user is relaxed, they can speak at a slower speed, and if they are nervous, they can speak at a slower speed. Also, if the user is angry, the volume can be increased, making it possible to generate voices according to the user's emotional state. This makes it possible to generate voices that correspond to the user's emotional state, resulting in more natural voices.

[0084] In addition to learning the user's speaking habits and rhythm, it can also analyze the user's past conversation data to learn the optimal speaking speed and volume. For example, it can collect phrases and expressions that the user uses daily and learn their speaking habits and rhythm. It can also analyze the tempo of past conversations to learn the optimal speaking speed. In this way, by learning the user's speaking habits and rhythm, it becomes possible to generate more natural voice.

[0085] In addition to learning changes in the user's voice, the system can also monitor the user's heart rate and stress level and adjust the voice generation based on that data. For example, if the user is nervous, the system will speak at a slower speed, and if they are relaxed, it will speak at a normal speed. It is also possible to generate voices that correspond to the heart rate and stress level, such as lowering the volume when the heart rate is high. This makes it possible to generate more natural voices by monitoring the user's heart rate and stress level.

[0086] Not only can it add appropriate emotional expressions depending on the content of the manuscript, but it can also understand the context of the manuscript and generate speech that emphasizes important parts. For example, it can analyze the content of the manuscript and generate speech that emphasizes keywords and phrases. The generation AI can also understand the context of the manuscript and emphasize important parts. This allows for more effective communication by understanding the context of the manuscript and emphasizing important parts.

[0087] Not only can appropriate background sounds and sound effects be automatically added based on the content of the manuscript, but the background sounds and sound effects can also be adjusted according to the user's emotional state. For example, calm music can be added when the user is relaxed, and relaxing music can be added when the user is tense. Also, cheerful music can be added when the user is happy. This makes it possible to adjust background sounds and sound effects according to the user's emotional state, thereby realizing more realistic audio generation.

[0088] The manuscript input unit not only automatically converts what the user has said into text using speech recognition technology, but also converts the text into text that reflects the user's speaking habits and rhythm. For example, the text can be converted into text that reflects the user's habit of emphasizing certain words. It can also be converted into text that reflects the user's speaking rhythm. This makes it possible to convert the text into text that reflects the user's speaking habits and rhythm, resulting in more natural conversion.

[0089] The manuscript input unit not only visually displays the contents of the manuscript, but can also adjust the display method according to the user's emotional state. For example, when the user is relaxed, the content is displayed in gentle colors, and when the user is nervous, the content is displayed in subdued colors. Also, when the user is happy, the content is displayed in bright colors. This makes it possible to adjust the display method according to the user's emotional state, thereby realizing a more visually easy-to-understand interface.

[0090] The playback control unit not only monitors the user's emotional state in real time and suggests audio playback at the appropriate time, but also links with the user's calendar and schedule to automatically play audio before important events or meetings. For example, a reminder audio can be played five minutes before the start of a meeting. The generation AI can also analyze the user's calendar and schedule and adjust the timing of audio playback. This makes it possible to automatically play audio before important events or meetings by linking the timing of audio playback with the user's calendar and schedule.

[0091] The playback control unit can not only link the timing of audio playback with the user's location information, but also adjust the playback timing according to the user's emotional state. For example, when the user is tense, it can play relaxing audio, and when the user is relaxed, it can play normal audio. The generation AI can also analyze the user's location information and emotional state and adjust the timing of audio playback. By linking the timing of audio playback with location information and emotional state, more effective information transmission is possible.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The voice learning unit learns the user's voice. For example, the user reads some sample sentences and inputs the resulting voice data into the generation AI. The generation AI then analyzes this voice data and learns the characteristics of the user's voice. Specifically, it analyzes the voice frequency, volume, and voice quality. Step 2: The manuscript input unit receives text entered by the user. For example, the user inputs the manuscript they want to read out loud during a phone call in text format. It is also possible to automatically convert what the user says into text using voice recognition technology. Specifically, voice recognition technology is used to convert what the user says into text in real time. Step 3: The speech generation unit generates speech in the user's voice from the text received by the manuscript input unit. For example, the generation AI generates speech using the trained user's voice. The generation AI can also add appropriate emotional expressions depending on the content of the manuscript, achieving an emotionally rich reading. Specifically, the generation AI analyzes the content of the manuscript and adds emotional expressions. Step 4: The playback control unit plays the audio generated by the audio generation unit at any time. For example, after the user says, "I will now read the manuscript," pressing a button will play the audio in which the AI ​​reads the manuscript. The playback control unit can also monitor the user's emotional state in real time and suggest audio playback at an appropriate time. Specifically, it uses facial recognition technology to analyze facial expressions and estimate the user's emotional state.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[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 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.

[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 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.

[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 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 learning unit that learns the user's voice; a manuscript input unit that receives text entered by a user; a voice generating unit that generates voice in a user's voice from the text received by the manuscript input unit; a playback control unit that plays back the sound generated by the sound generation unit at any timing. A system characterized by:

2. The voice learning unit Estimating the emotional state of the user and learning a tone and intonation of voice corresponding to the emotional state.

2. The system of claim 1.

3. The voice generation unit Adds appropriate emotional expressions according to the content of the manuscript, resulting in an emotionally rich reading 2. The system of claim 1.

4. The manuscript input unit Using voice recognition technology, what the user says is automatically converted into text.

2. The system of claim 1.

5. The playback control unit Monitoring the user's emotional state in real time and suggesting audio playback at an appropriate time based on the emotional state.

2. The system of claim 1.

6. The voice generation unit Automatically adjusting the speaking speed according to the emotional state of the user.

2. The system of claim 1.

7. The playback control unit Analyze the user's emotional state in real time within the calling app and generate appropriate voice.

2. The system of claim 1.

8. The playback control unit Analyze the emotional state of the person on the other end of the call and suggest appropriate responses 2. The system of claim 1.

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