System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system includes a voice recognition unit, a text generation unit, and a display unit. The voice recognition unit analyzes the voice of the telephone in real time. The text generation unit converts the voice analyzed by the voice recognition unit into text. The display unit displays the text generated by the text generation unit on a television screen.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] With conventional technology, there was a problem that telephone voices were difficult to hear due to factors such as surrounding noise, line conditions, and the other party's unclear speech.

[0005] The system according to the embodiment aims to enable visual understanding of telephone voices. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a text generation unit, and a display unit. The speech recognition unit analyzes telephone speech in real time. The text generation unit converts the speech analyzed by the speech recognition unit into text. The display unit displays the text generated by the text generation unit on a television screen. [Effects of the Invention]

[0007] The system according to the embodiment can provide visual understanding of the audio on the telephone. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The system according to the embodiment of the present invention solves the problem of difficulty in hearing telephone conversations by converting the voice of a telephone call into text and displaying it on a television screen, thereby enabling the contents of the call to be visually confirmed.

[0029] According to an embodiment, a system for resolving difficulties in listening to telephone calls includes a speech recognition unit, a text generation unit, and a display unit. The speech recognition unit analyzes speech over the phone in real time. For example, the speech recognition unit analyzes speech data using deep learning technology. The speech recognition unit can also analyze speech data using a hidden Markov model (HMM). The speech recognition unit can analyze speech data in real time and minimize delays. For example, the speech recognition unit analyzes speech data with a delay of milliseconds. The text generation unit converts speech analyzed by the speech recognition unit into text. For example, the text generation unit converts speech data into text while maintaining grammatical consistency. The text generation unit can also generate text by appropriately inserting punctuation marks. The text generation unit converts speech data into text with high accuracy. For example, the text generation unit generates text that accurately reflects the content of the speech data. The display unit displays the text generated by the text generation unit on a television screen. For example, the display unit adjusts the font size to display the text. The display unit can also adjust the display position to display the text. The display unit can also adjust the color of the text when displaying it. For example, the display unit can appropriately set the background color and the color of the text to improve visibility. This allows the system for solving the problem of difficulty in hearing telephone calls according to the embodiment to visually check the contents of the telephone call. For example, by checking the contents of the telephone call in text, it is possible to compensate for the parts that are difficult to hear. Furthermore, by visually checking the contents of the telephone call, there is no need to listen to it multiple times.

[0030] The voice recognition unit can filter ambient noise from the voice data to generate clear voice data. The voice recognition unit filters ambient noise using, for example, noise canceling technology. For example, the voice recognition unit analyzes ambient noise in real time and performs noise cancellation. The voice recognition unit can also filter noise using spectral subtraction technology. For example, the voice recognition unit analyzes the spectrum of the voice data and subtracts noise components. The voice recognition unit can also use a combination of multiple filtering technologies to generate clear voice data. For example, the voice recognition unit combines noise canceling and spectral subtraction to remove noise. This allows the voice recognition unit to remove ambient noise and generate clear voice data.

[0031] The voice recognition unit can monitor the status of the telephone line and display a warning if the line status is poor. The voice recognition unit, for example, measures signal strength to monitor the status of the telephone line. For example, the voice recognition unit displays a warning if the signal strength decreases. The voice recognition unit can also monitor the line status by analyzing the error rate. For example, the voice recognition unit displays a warning if the error rate increases. The voice recognition unit can also evaluate the quality of the line based on the results of monitoring the line status. For example, the voice recognition unit displays a warning if the line quality decreases. This allows visual confirmation that the line status is poor.

[0032] The speech recognition unit can analyze the other party's articulation and display a message to that effect if the other party's articulation is poor. The speech recognition unit analyzes the other party's articulation using, for example, phoneme identification technology. For example, the speech recognition unit evaluates the quality of articulation based on the phoneme identification results. The speech recognition unit can also analyze the clarity of pronunciation to evaluate articulation. For example, the speech recognition unit determines that articulation is poor if the articulation is low. The speech recognition unit also displays a message to that effect if the other party's articulation is poor based on the analysis results of articulation. For example, the speech recognition unit displays a message saying "The other party's articulation is poor" if the other party's articulation is poor. This allows the user to visually confirm that the other party's articulation is poor.

[0033] The speech recognition unit can automatically detect a speaker's accent or dialect and convert it into standard Japanese. The speech recognition unit can detect a speaker's accent by analyzing, for example, phonetic patterns. For example, the speech recognition unit can detect the phonetic patterns of Kansai dialect and convert it into standard Japanese. The speech recognition unit can also detect dialects by analyzing regional pronunciation. For example, the speech recognition unit can detect dialects from the Tohoku region and convert them into standard Japanese. The speech recognition unit can also convert dialects into standard Japanese using dictionary-based conversion technology. For example, the speech recognition unit can convert dialect words into standard Japanese by referring to a dictionary. The speech recognition unit can also convert dialects into standard Japanese using a speech model. For example, the speech recognition unit can convert dialects into standard Japanese by learning a speech model. This allows the speaker's accent or dialect to be converted into standard Japanese.

[0034] The speech recognition unit can understand the speaker's intention and convert ambiguous expressions into clear expressions. The speech recognition unit can understand the speaker's intention using, for example, natural language processing technology. For example, the speech recognition unit can analyze the context to understand the speaker's intention. The speech recognition unit can also convert ambiguous expressions into clear expressions using context analysis technology. For example, the speech recognition unit can specifically convert "that" into "that product." The speech recognition unit can also convert ambiguous expressions into clear expressions using synonyms. For example, the speech recognition unit can convert "that" into "that material." The speech recognition unit can also perform context-based interpretation to convert ambiguous expressions into clear expressions. For example, the speech recognition unit can convert "this" into "this project." In this way, ambiguous expressions can be converted into clear expressions.

[0035] The speech recognition unit can be applied not only to telephone calls but also to video calls or online conferences, allowing visual confirmation. For example, the speech recognition unit can be applied to video calls using Zoom or Teams, converting the speaker's speech into text in real time and displaying it on the screen. For example, the speech recognition unit can convert the speaker's remarks into text and display them during a video call. The speech recognition unit can also convert speech during an online conference into text so that all participants can visually confirm the content. For example, the speech recognition unit can convert the speaker's remarks into text and display them during an online conference. The speech recognition unit can also enhance speech recognition technology in video calls and online conferences using Webex or Skype, converting the speaker's remarks into text in real time and displaying them on the screen. For example, the speech recognition unit can convert the speaker's remarks into text and display them during a video call or online conference. This allows visual confirmation of the content even during video calls and online conferences.

[0036] The speech recognition unit can convert the content of lectures and seminars in educational settings into text in real time, allowing them to be visually confirmed. For example, the speech recognition unit can convert university lectures into text in real time, allowing students to visually confirm the content. For example, the speech recognition unit can convert a professor's remarks during a lecture into text and display them. The speech recognition unit can also convert the content of a business seminar into text in real time, allowing participants to visually confirm the content. For example, the speech recognition unit can convert a lecturer's remarks during a seminar into text and display them. The speech recognition unit can also convert the content of lectures and seminars on online education platforms into text in real time, allowing them to be visually confirmed. For example, the speech recognition unit can convert a lecturer's remarks during an online lecture into text and display them. This allows the content to be visually confirmed even in educational settings and seminars.

[0037] The display unit can add a function that allows the user to customize the text displayed on the television screen according to the user's preferences. The display unit can add a function that allows the user to customize, for example, the font and color of the text displayed on the television screen. For example, the display unit can allow the user to select their preferred font and color. The display unit can also add a function that allows the user to adjust the display position and size of the text according to the user's preferences. For example, the display position of the text can be changed to the top or bottom of the screen. The display unit can also add a function that allows the user to customize the style of the text displayed on the television screen. For example, the display unit can allow the user to adjust the background color and transparency of the text. This makes it possible to display text according to the user's preferences.

[0038] The display unit can add a function to highlight important keywords in text displayed on the television screen. For example, the display unit can add a function to automatically detect and highlight important keywords in text displayed on the television screen. For example, it can highlight important words in a conversation. The display unit can also add a function to allow a user to set important keywords and highlight text containing those keywords. For example, it can highlight specific words or phrases. The display unit can also add a function to highlight important keywords in text displayed on the television screen using a color or font style. For example, it can display important words in bold or a different color. This allows important keywords to be visually highlighted.

[0039] The display unit can synchronize the display of text with other devices, such as a smartphone or tablet, so that the text can be visually confirmed on multiple devices. For example, the display unit can synchronize text displayed on a television screen with a smartphone or tablet so that the text can be visually confirmed on multiple devices. For example, the same text can be displayed on a television and a smartphone. The display unit can also develop a system that synchronizes the display of text with other devices in real time. For example, the text can be displayed simultaneously on a television screen and a tablet. The display unit can also automatically send text displayed on a television screen to a smartphone or tablet so that the text can be visually confirmed on multiple devices. For example, the same text can be displayed on a television and a tablet. This allows the text to be visually confirmed on multiple devices.

[0040] The display unit can automatically translate displayed text into different languages ​​to support international communication. For example, the display unit adds a function to automatically translate text displayed on a television screen into different languages. For example, Japanese text can be translated into English and displayed. The display unit can also use the automatic translation function to translate text displayed on a television screen into multiple languages ​​to support international communication. For example, Japanese text can be translated into English and Chinese and displayed. The display unit can also enhance its automatic text translation function to develop a system that translates and displays text into different languages ​​in real time. For example, Japanese text can be translated into English and displayed. This allows automatic translation into different languages ​​to support international communication.

[0041] The speech recognition unit can emphasize specific sounds from the surrounding sounds and suppress other sounds. For example, the speech recognition unit can use generative AI to analyze surrounding sounds and add functions to emphasize the speaker's voice and suppress other sounds. For example, it can make the speaker's voice clear even in noisy environments. The speech recognition unit can also develop algorithms that emphasize specific sounds and suppress other sounds. For example, it can emphasize the speaker's voice and suppress background noise. The speech recognition unit can also use generative AI to build a system that extracts the speaker's voice from the surrounding sounds and suppresses other sounds. For example, it can make the speaker's voice clear even in noisy places such as cafes and train stations. This makes it possible to emphasize specific sounds and suppress other sounds.

[0042] The voice recognition unit can automatically identify the type of sound and process it appropriately when filtering surrounding sounds. For example, the voice recognition unit adds a function in which the generation AI analyzes surrounding sounds and automatically identifies the type of sound. For example, it can identify the sound of cars and the sound of wind and perform appropriate filtering for each. The voice recognition unit can also develop algorithms that automatically identify the type of sound and process it appropriately. For example, it can suppress the sound of cars and reduce the sound of wind. The voice recognition unit can also use the generation AI to build a system that identifies the type of surrounding sound and filters it appropriately. For example, it can effectively suppress specific sounds even in noisy environments. This allows the type of sound to be automatically identified and processed appropriately.

[0043] The voice recognition unit can apply ambient sound filtering technology when recording music or podcasts to provide clear audio. The voice recognition unit can, for example, apply ambient sound filtering technology when recording music to provide clear audio. For example, high-quality audio can be achieved even when recording outside a studio. The voice recognition unit can also apply ambient sound filtering technology when recording podcasts to provide clear audio. For example, clear audio can be recorded even in noisy environments. The voice recognition unit can also build a system that enhances ambient sound filtering technology when recording music or podcasts to provide clear audio. For example, high-quality audio can be achieved even when recording outdoors. This allows clear audio to be provided even when recording music or podcasts.

[0044] The voice recognition unit can apply ambient sound filtering technology to support communication in noisy environments. The voice recognition unit can apply ambient sound filtering technology to support communication at, for example, a construction site to provide clear audio. For example, noise at a construction site can be suppressed to facilitate communication between workers. The voice recognition unit can also apply ambient sound filtering technology to support communication in noisy environments. For example, noise in a factory can be suppressed to make conversations between workers clearer. The voice recognition unit can also strengthen ambient sound filtering technology to build a system that can be applied to support communication in noisy environments. For example, noise at a construction site can be suppressed to facilitate communication between workers. This makes it possible to support communication with clear audio even in noisy environments.

[0045] The speech recognition unit can analyze fluctuations in line status in real time and provide advance warning of predicted problems. For example, the speech recognition unit adds a function in which a generation AI analyzes fluctuations in line status in real time and provides advance warning of predicted problems. For example, a warning is displayed when a line delay or disconnection is predicted. The speech recognition unit can also develop an algorithm that analyzes fluctuations in line status in real time and provides advance warning of predicted problems. For example, a warning is displayed before line quality deteriorates. The speech recognition unit can also use a generation AI to build a system that analyzes fluctuations in line status in real time and provides advance warning of predicted problems. For example, a warning is displayed before the line bandwidth becomes insufficient. This makes it possible to analyze fluctuations in line status in real time and provide advance warning of predicted problems.

[0046] When monitoring line status, the voice recognition unit can analyze line usage history and automatically adjust optimal communication settings. For example, the voice recognition unit adds a function whereby a generation AI analyzes line usage history and automatically adjusts optimal communication settings. For example, it sets the optimal bandwidth based on past usage patterns. The voice recognition unit can also develop an algorithm that analyzes line usage history and automatically adjusts optimal communication settings. For example, it sets the optimal communication settings based on peak usage. The voice recognition unit can also use generation AI to build a system that analyzes line usage history and automatically adjusts optimal communication settings. For example, it sets the optimal communication settings based on past data. This makes it possible to analyze line usage history and automatically adjust optimal communication settings.

[0047] The voice recognition unit can apply the line status monitoring technology to all Internet connections to improve network quality within a home. The voice recognition unit can apply the line status monitoring technology to, for example, all Internet connections within a home to improve network quality. For example, the voice recognition unit can monitor the quality of Wi-Fi connections within a home in real time. The voice recognition unit can also apply the line status monitoring technology to all Internet connections to build a system that improves network quality within a home. For example, the voice recognition unit can monitor the quality of wired and wireless connections within a home. The voice recognition unit can also enhance the line status monitoring technology to develop a system that improves network quality within a home. For example, the voice recognition unit can analyze network traffic within a home and set optimal settings. This can improve network quality within a home.

[0048] The speech recognition unit can apply line status monitoring technology to a company's communications infrastructure to improve business efficiency. The speech recognition unit can apply line status monitoring technology to a company's communications infrastructure to improve business efficiency. For example, it can monitor network quality within a company in real time and respond immediately if a problem occurs. The speech recognition unit can also apply line status monitoring technology to a company's communications infrastructure to build a system that improves business efficiency. For example, it can analyze network traffic within a company and perform optimal settings. The speech recognition unit can also enhance line status monitoring technology to develop a system that improves the quality of a company's communications infrastructure. For example, it can monitor network quality within a company in real time and respond immediately if a problem occurs. This can improve the quality of a company's communications infrastructure and improve business efficiency.

[0049] The speech recognition unit can provide real-time feedback to speakers to improve their pronunciation based on the analysis results of the other person's enunciation. For example, the speech recognition unit uses a generation AI to analyze the other person's enunciation and, based on the results, provides real-time feedback to the speaker to improve their enunciation. For example, it can suggest pronunciation practice methods. The speech recognition unit can also develop a system that provides specific points for improvement to speakers in real time based on the analysis results of enunciation. For example, it can instruct the speaker to emphasize the pronunciation of specific sounds. The speech recognition unit can also use a generation AI to provide real-time feedback to speakers based on the analysis results of the other person's enunciation and give advice to improve their enunciation. For example, it can teach them pronunciation tips. In this way, it is possible to provide real-time feedback to speakers to improve their enunciation based on the analysis results of the other person's enunciation.

[0050] When analyzing the other person's enunciation, the speech recognition unit can learn the speaker's pronunciation characteristics and perform individually optimized analysis. For example, when the generation AI analyzes the other person's enunciation, the speech recognition unit adds a function to learn the speaker's pronunciation characteristics and perform individually optimized analysis. For example, it learns the speaker's pronunciation patterns and improves analysis accuracy. The speech recognition unit can also learn the speaker's pronunciation characteristics and develop an algorithm to perform individually optimized enunciation analysis. For example, it performs analysis taking into account the speaker's pronunciation habits. The speech recognition unit can also use the generation AI to build a system that learns the speaker's pronunciation characteristics and performs individually optimized enunciation analysis. For example, it performs analysis taking into account the pronunciation patterns that differ for each speaker. This makes it possible to learn the speaker's pronunciation characteristics and perform individually optimized enunciation analysis.

[0051] The speech recognition unit can apply the articulation analysis technology to the fields of speech therapy or pronunciation instruction to provide effective training. The speech recognition unit can, for example, apply the articulation analysis technology to speech therapy to provide effective training. For example, it can identify pronunciation problems and suggest practice methods for improvement. The speech recognition unit can also apply the articulation analysis technology to the field of pronunciation instruction to build a system that provides effective training. For example, it can analyze pronunciation problems and provide guidance on specific points to improve. The speech recognition unit can also enhance the articulation analysis technology to develop a system that can be applied to the fields of speech therapy or pronunciation instruction. For example, it can identify pronunciation problems and suggest effective training methods. This allows the articulation analysis technology to be applied to the fields of speech therapy or pronunciation instruction to provide effective training.

[0052] The speech recognition unit can apply the articulation analysis technology to a voice assistant or a navigation system to provide an optimal response according to the user's pronunciation. The speech recognition unit can, for example, apply the articulation analysis technology to a voice assistant to provide an optimal response according to the user's pronunciation. For example, an accurate response can be provided even if the pronunciation is unclear. The speech recognition unit can also apply the articulation analysis technology to a navigation system to build a system that provides an optimal response according to the user's pronunciation. For example, accurate route guidance can be provided even if the pronunciation is unclear. The speech recognition unit can also enhance the articulation analysis technology to develop a system to be applied to a voice assistant or a navigation system. For example, an accurate response can be provided even if the pronunciation is unclear. This allows the articulation analysis technology to be applied to a voice assistant or a navigation system to provide an optimal response according to the user's pronunciation.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The speech recognition unit can automatically detect a speaker's accent or dialect and convert it into standard Japanese. For example, the speech recognition unit detects a speaker's accent by analyzing phonological patterns. For example, the speech recognition unit detects the phonological patterns of Kansai dialect and converts it into standard Japanese. The speech recognition unit can also detect dialects by analyzing regional pronunciation. For example, the speech recognition unit detects dialects from the Tohoku region and converts them into standard Japanese. The speech recognition unit can also convert dialects into standard Japanese using dictionary-based conversion technology. For example, the speech recognition unit converts dialect words into standard Japanese by looking up words in a dictionary. The speech recognition unit can also convert dialects into standard Japanese using a speech model. For example, the speech recognition unit trains a speech model to convert dialects into standard Japanese. This allows the speaker's accent or dialect to be converted into standard Japanese.

[0055] The speech recognition unit can understand the speaker's intention and convert ambiguous expressions into clear expressions. For example, the speech recognition unit understands the speaker's intention using natural language processing technology. For example, the speech recognition unit analyzes the context to understand the speaker's intention. The speech recognition unit can also convert ambiguous expressions into clear expressions using context analysis technology. For example, the speech recognition unit specifically converts "that" into "that product." The speech recognition unit can also convert ambiguous expressions into clear expressions using synonyms. For example, the speech recognition unit converts "that" into "that material." The speech recognition unit can also perform context-based interpretation to convert ambiguous expressions into clear expressions. For example, the speech recognition unit converts "this" into "this project." In this way, ambiguous expressions can be converted into clear expressions.

[0056] The speech recognition unit can be applied not only to telephone calls but also to video calls or online conferences, allowing for visual confirmation. For example, it can be applied to video calls using Zoom or Teams, converting the speaker's speech into text in real time and displaying it on the screen. For example, the speaker's remarks during a video call can be converted into text and displayed on the screen. The speech recognition unit can also convert speech during online conferences into text so that all participants can visually confirm the content. For example, the speaker's remarks can be converted into text and displayed on the screen during an online conference. The speech recognition unit can also enhance speech recognition technology in video calls and online conferences using Webex or Skype, converting the speaker's remarks into text in real time and displaying it on the screen. For example, the speaker's remarks can be converted into text and displayed on the screen during a video call or online conference. This allows for visual confirmation of the content even during video calls or online conferences.

[0057] The speech recognition unit can convert the content of lectures and seminars in educational settings into text in real time, allowing them to be visually confirmed. For example, university lectures can be converted into text in real time, allowing students to visually confirm the content. For example, the professor's remarks during a lecture can be converted into text and displayed. The speech recognition unit can also convert the content of a business seminar into text in real time, allowing participants to visually confirm the content. For example, the lecturer's remarks during a seminar can be converted into text and displayed. The speech recognition unit can also convert the content of lectures and seminars on online education platforms into text in real time, allowing them to be visually confirmed. For example, the lecturer's remarks during an online lecture can be converted into text and displayed. This allows the content to be visually confirmed even in educational settings and seminars.

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

[0059] Step 1: The speech recognition unit analyzes the phone call in real time. For example, the speech recognition unit analyzes the voice data using deep learning technology or HMM (Hidden Markov Model), and analyzes the voice data with a delay of milliseconds. Step 2: The text generator converts the speech analyzed by the speech recognizer into text. For example, the text generator converts the speech data into text while maintaining grammatical consistency and inserts punctuation marks appropriately to generate text with high accuracy. Step 3: The display unit displays the text generated by the text generator on the television screen. For example, the display unit adjusts the font size, display position, and color of the text to improve visibility.

[0060] (Example 2) The system according to the embodiment of the present invention solves the problem of difficulty in hearing telephone conversations by converting the voice of a telephone call into text and displaying it on a television screen, thereby enabling the contents of the call to be visually confirmed.

[0061] According to an embodiment, a system for resolving difficulties in listening to telephone calls includes a speech recognition unit, a text generation unit, and a display unit. The speech recognition unit analyzes speech over the phone in real time. For example, the speech recognition unit analyzes speech data using deep learning technology. The speech recognition unit can also analyze speech data using a hidden Markov model (HMM). The speech recognition unit can analyze speech data in real time and minimize delays. For example, the speech recognition unit analyzes speech data with a delay of milliseconds. The text generation unit converts speech analyzed by the speech recognition unit into text. For example, the text generation unit converts speech data into text while maintaining grammatical consistency. The text generation unit can also generate text by appropriately inserting punctuation marks. The text generation unit converts speech data into text with high accuracy. For example, the text generation unit generates text that accurately reflects the content of the speech data. The display unit displays the text generated by the text generation unit on a television screen. For example, the display unit adjusts the font size to display the text. The display unit can also adjust the display position to display the text. The display unit can also adjust the color of the text when displaying it. For example, the display unit can appropriately set the background color and the color of the text to improve visibility. This allows the system for solving the problem of difficulty in hearing telephone calls according to the embodiment to visually check the contents of the telephone call. For example, by checking the contents of the telephone call in text, it is possible to compensate for the parts that are difficult to hear. Furthermore, by visually checking the contents of the telephone call, there is no need to listen to it multiple times.

[0062] The voice recognition unit can filter ambient noise from the voice data to generate clear voice data. The voice recognition unit filters ambient noise using, for example, noise canceling technology. For example, the voice recognition unit analyzes ambient noise in real time and performs noise cancellation. The voice recognition unit can also filter noise using spectral subtraction technology. For example, the voice recognition unit analyzes the spectrum of the voice data and subtracts noise components. The voice recognition unit can also use a combination of multiple filtering technologies to generate clear voice data. For example, the voice recognition unit combines noise canceling and spectral subtraction to remove noise. This allows the voice recognition unit to remove ambient noise and generate clear voice data.

[0063] The voice recognition unit can monitor the status of the telephone line and display a warning if the line status is poor. The voice recognition unit, for example, measures signal strength to monitor the status of the telephone line. For example, the voice recognition unit displays a warning if the signal strength decreases. The voice recognition unit can also monitor the line status by analyzing the error rate. For example, the voice recognition unit displays a warning if the error rate increases. The voice recognition unit can also evaluate the quality of the line based on the results of monitoring the line status. For example, the voice recognition unit displays a warning if the line quality decreases. This allows visual confirmation that the line status is poor.

[0064] The speech recognition unit can analyze the other party's articulation and display a message to that effect if the other party's articulation is poor. The speech recognition unit analyzes the other party's articulation using, for example, phoneme identification technology. For example, the speech recognition unit evaluates the quality of articulation based on the phoneme identification results. The speech recognition unit can also analyze the clarity of pronunciation to evaluate articulation. For example, the speech recognition unit determines that articulation is poor if the articulation is low. The speech recognition unit also displays a message to that effect if the other party's articulation is poor based on the analysis results of articulation. For example, the speech recognition unit displays a message saying "The other party's articulation is poor" if the other party's articulation is poor. This allows the user to visually confirm that the other party's articulation is poor.

[0065] The speech recognition unit estimates the speaker's emotion and changes the color or font of the text according to the emotion, thereby visually conveying the nuances of the emotion. The speech recognition unit, for example, estimates the speaker's emotion by analyzing the tone of the voice. For example, the speech recognition unit estimates the emotion of joy when the tone of the voice is high. The speech recognition unit can also estimate the emotion by analyzing the pitch of the voice. For example, the speech recognition unit estimates the emotion of sadness when the pitch of the voice is low. The speech recognition unit can also estimate the emotion by analyzing the speed of the voice. For example, the speech recognition unit estimates the emotion of anger when the speed of the voice is fast. The speech recognition unit changes the color or font of the text according to the estimated emotion. For example, if the emotion of joy is strong, the text is made a bright color. The speech recognition unit also applies a font style according to the emotion. For example, if the emotion of anger is strong, the text is made bold. The speech recognition unit can also change the background color of the text based on the emotion estimation result. For example, if the emotion is positive, the background color is made brighter, and if the emotion is negative, the background color is made darker. This allows the nuances of the speaker's emotions to be visually conveyed.

[0066] The speech recognition unit can automatically detect a speaker's accent or dialect and convert it into standard Japanese. The speech recognition unit can detect a speaker's accent by analyzing, for example, phonetic patterns. For example, the speech recognition unit can detect the phonetic patterns of Kansai dialect and convert it into standard Japanese. The speech recognition unit can also detect dialects by analyzing regional pronunciation. For example, the speech recognition unit can detect dialects from the Tohoku region and convert them into standard Japanese. The speech recognition unit can also convert dialects into standard Japanese using dictionary-based conversion technology. For example, the speech recognition unit can convert dialect words into standard Japanese by referring to a dictionary. The speech recognition unit can also convert dialects into standard Japanese using a speech model. For example, the speech recognition unit can convert dialects into standard Japanese by learning a speech model. This allows the speaker's accent or dialect to be converted into standard Japanese.

[0067] The speech recognition unit can understand the speaker's intention and convert ambiguous expressions into clear expressions. The speech recognition unit can understand the speaker's intention using, for example, natural language processing technology. For example, the speech recognition unit can analyze the context to understand the speaker's intention. The speech recognition unit can also convert ambiguous expressions into clear expressions using context analysis technology. For example, the speech recognition unit can specifically convert "that" into "that product." The speech recognition unit can also convert ambiguous expressions into clear expressions using synonyms. For example, the speech recognition unit can convert "that" into "that material." The speech recognition unit can also perform context-based interpretation to convert ambiguous expressions into clear expressions. For example, the speech recognition unit can convert "this" into "this project." In this way, ambiguous expressions can be converted into clear expressions.

[0068] The speech recognition unit can be applied not only to telephone calls but also to video calls or online conferences, allowing visual confirmation. For example, the speech recognition unit can be applied to video calls using Zoom or Teams, converting the speaker's speech into text in real time and displaying it on the screen. For example, the speech recognition unit can convert the speaker's remarks into text and display them during a video call. The speech recognition unit can also convert speech during an online conference into text so that all participants can visually confirm the content. For example, the speech recognition unit can convert the speaker's remarks into text and display them during an online conference. The speech recognition unit can also enhance speech recognition technology in video calls and online conferences using Webex or Skype, converting the speaker's remarks into text in real time and displaying them on the screen. For example, the speech recognition unit can convert the speaker's remarks into text and display them during a video call or online conference. This allows visual confirmation of the content even during video calls and online conferences.

[0069] The speech recognition unit can convert the content of lectures and seminars in educational settings into text in real time, allowing them to be visually confirmed. For example, the speech recognition unit can convert university lectures into text in real time, allowing students to visually confirm the content. For example, the speech recognition unit can convert a professor's remarks during a lecture into text and display them. The speech recognition unit can also convert the content of a business seminar into text in real time, allowing participants to visually confirm the content. For example, the speech recognition unit can convert a lecturer's remarks during a seminar into text and display them. The speech recognition unit can also convert the content of lectures and seminars on online education platforms into text in real time, allowing them to be visually confirmed. For example, the speech recognition unit can convert a lecturer's remarks during an online lecture into text and display them. This allows the content to be visually confirmed even in educational settings and seminars.

[0070] The speech recognition unit uses the emotion estimation function to provide real-time feedback according to the speaker's emotion, thereby improving the quality of communication. The speech recognition unit, for example, uses the emotion estimation function to analyze the speaker's emotion in real time and provide feedback according to the emotion. For example, if the speaker is nervous, a message urging the speaker to relax is displayed. The speech recognition unit also develops a system that analyzes the speaker's emotion and provides feedback according to the emotion. For example, if the speaker is happy, positive feedback is displayed. The speech recognition unit also uses the emotion estimation function to provide real-time feedback according to the speaker's emotion, thereby improving the quality of communication. For example, if the speaker is angry, a message urging the speaker to stay calm is displayed. This allows for providing feedback according to the speaker's emotion and improving the quality of communication.

[0071] The display unit can use the emotion estimation function to change the color and font of text displayed on the television screen to reflect the speaker's emotions. The display unit, for example, uses the emotion estimation function to analyze the speaker's emotions and change the color of the text according to the emotion. For example, if the emotion of joy is strong, the color is changed to a brighter color. The display unit can also use the emotion estimation function to change the font style of the text. For example, if the emotion of anger is strong, the font is changed to bold. The display unit can also use the emotion estimation function to change the background color of the text. For example, if the emotion is positive, the background color is changed to a brighter color, and if the emotion is negative, the background color is changed to a darker color. This makes it possible to display text that reflects the speaker's emotions.

[0072] The display unit can add a function that allows the user to customize the text displayed on the television screen according to the user's preferences. The display unit can add a function that allows the user to customize, for example, the font and color of the text displayed on the television screen. For example, the display unit can allow the user to select their preferred font and color. The display unit can also add a function that allows the user to adjust the display position and size of the text according to the user's preferences. For example, the display position of the text can be changed to the top or bottom of the screen. The display unit can also add a function that allows the user to customize the style of the text displayed on the television screen. For example, the display unit can allow the user to adjust the background color and transparency of the text. This makes it possible to display text according to the user's preferences.

[0073] The display unit can add a function to highlight important keywords in text displayed on the television screen. For example, the display unit can add a function to automatically detect and highlight important keywords in text displayed on the television screen. For example, it can highlight important words in a conversation. The display unit can also add a function to allow a user to set important keywords and highlight text containing those keywords. For example, it can highlight specific words or phrases. The display unit can also add a function to highlight important keywords in text displayed on the television screen using a color or font style. For example, it can display important words in bold or a different color. This allows important keywords to be visually highlighted.

[0074] The display unit can synchronize the display of text with other devices, such as a smartphone or tablet, so that the text can be visually confirmed on multiple devices. For example, the display unit can synchronize text displayed on a television screen with a smartphone or tablet so that the text can be visually confirmed on multiple devices. For example, the same text can be displayed on a television and a smartphone. The display unit can also develop a system that synchronizes the display of text with other devices in real time. For example, the text can be displayed simultaneously on a television screen and a tablet. The display unit can also automatically send text displayed on a television screen to a smartphone or tablet so that the text can be visually confirmed on multiple devices. For example, the same text can be displayed on a television and a tablet. This allows the text to be visually confirmed on multiple devices.

[0075] The display unit can automatically translate displayed text into different languages ​​to support international communication. For example, the display unit adds a function to automatically translate text displayed on a television screen into different languages. For example, Japanese text can be translated into English and displayed. The display unit can also use the automatic translation function to translate text displayed on a television screen into multiple languages ​​to support international communication. For example, Japanese text can be translated into English and Chinese and displayed. The display unit can also enhance its automatic text translation function to develop a system that translates and displays text into different languages ​​in real time. For example, Japanese text can be translated into English and displayed. This allows automatic translation into different languages ​​to support international communication.

[0076] The display unit can use the emotion estimation function to collect the user's emotional reactions to the displayed text and optimize the display method. For example, the display unit can use the emotion estimation function to collect the user's emotional reactions to the displayed text and optimize the display method based on the data. For example, the display unit can adopt a display method that has a high percentage of positive emotional reactions. The display unit can also develop a system that analyzes the user's emotional reactions in real time and dynamically adjusts the text display method. For example, the display unit can change the color or font of the text depending on the user's emotion. The display unit can also develop an algorithm that optimizes the text display method based on the emotion estimation data. For example, the display unit can select the optimal display method based on the user's emotion score. This allows the display method to be optimized based on the user's emotional reactions.

[0077] The speech recognition unit can emphasize specific sounds from the surrounding sounds and suppress other sounds. For example, the speech recognition unit can use generative AI to analyze surrounding sounds and add functions to emphasize the speaker's voice and suppress other sounds. For example, it can make the speaker's voice clear even in noisy environments. The speech recognition unit can also develop algorithms that emphasize specific sounds and suppress other sounds. For example, it can emphasize the speaker's voice and suppress background noise. The speech recognition unit can also use generative AI to build a system that extracts the speaker's voice from the surrounding sounds and suppresses other sounds. For example, it can make the speaker's voice clear even in noisy places such as cafes and train stations. This makes it possible to emphasize specific sounds and suppress other sounds.

[0078] The voice recognition unit can automatically identify the type of sound and process it appropriately when filtering surrounding sounds. For example, the voice recognition unit adds a function in which the generation AI analyzes surrounding sounds and automatically identifies the type of sound. For example, it can identify the sound of cars and the sound of wind and perform appropriate filtering for each. The voice recognition unit can also develop algorithms that automatically identify the type of sound and process it appropriately. For example, it can suppress the sound of cars and reduce the sound of wind. The voice recognition unit can also use the generation AI to build a system that identifies the type of surrounding sound and filters it appropriately. For example, it can effectively suppress specific sounds even in noisy environments. This allows the type of sound to be automatically identified and processed appropriately.

[0079] When filtering ambient sounds, the speech recognition unit can use the emotion estimation function to perform speech processing according to the speaker's emotions. For example, when the generation AI filters ambient sounds, the speech recognition unit analyzes the speaker's emotions and performs speech processing according to the emotions. For example, if the speaker is nervous, noise is suppressed more strongly. The speech recognition unit can also use the emotion estimation function to develop an algorithm that performs speech processing according to the speaker's emotions. For example, if the speaker is relaxed, natural speech processing is performed. The speech recognition unit can also use the generation AI to build a system that performs speech processing according to the speaker's emotions. For example, if the speaker is angry, the speech is emphasized, and if the speaker is calm, the speech is suppressed. This makes it possible to perform speech processing according to the speaker's emotions.

[0080] The voice recognition unit can apply ambient sound filtering technology when recording music or podcasts to provide clear audio. The voice recognition unit can, for example, apply ambient sound filtering technology when recording music to provide clear audio. For example, high-quality audio can be achieved even when recording outside a studio. The voice recognition unit can also apply ambient sound filtering technology when recording podcasts to provide clear audio. For example, clear audio can be recorded even in noisy environments. The voice recognition unit can also build a system that enhances ambient sound filtering technology when recording music or podcasts to provide clear audio. For example, high-quality audio can be achieved even when recording outdoors. This allows clear audio to be provided even when recording music or podcasts.

[0081] The voice recognition unit can apply ambient sound filtering technology to support communication in noisy environments. The voice recognition unit can apply ambient sound filtering technology to support communication at, for example, a construction site to provide clear audio. For example, noise at a construction site can be suppressed to facilitate communication between workers. The voice recognition unit can also apply ambient sound filtering technology to support communication in noisy environments. For example, noise in a factory can be suppressed to make conversations between workers clearer. The voice recognition unit can also strengthen ambient sound filtering technology to build a system that can be applied to support communication in noisy environments. For example, noise at a construction site can be suppressed to facilitate communication between workers. This makes it possible to support communication with clear audio even in noisy environments.

[0082] The voice recognition unit can use the emotion estimation function to collect the user's emotional responses to the ambient sound filtering results and optimize the filtering algorithm. For example, the voice recognition unit can use the emotion estimation function to collect the user's emotional responses to the ambient sound filtering results and optimize the filtering algorithm based on the data. For example, the voice recognition unit can adopt filtering settings that have a high positive emotional response. The voice recognition unit can also develop a system that analyzes the user's emotional responses in real time and dynamically adjusts the filtering algorithm. For example, the voice recognition unit can adjust the filtering strength according to the user's emotions. The voice recognition unit can also develop an algorithm that optimizes the ambient sound filtering algorithm based on the emotion estimation data. For example, the voice recognition unit can select optimal filtering settings based on the user's emotion score. This allows the filtering algorithm to be optimized based on the user's emotional responses.

[0083] The speech recognition unit can analyze fluctuations in line status in real time and provide advance warning of predicted problems. For example, the speech recognition unit adds a function in which a generation AI analyzes fluctuations in line status in real time and provides advance warning of predicted problems. For example, a warning is displayed when a line delay or disconnection is predicted. The speech recognition unit can also develop an algorithm that analyzes fluctuations in line status in real time and provides advance warning of predicted problems. For example, a warning is displayed before line quality deteriorates. The speech recognition unit can also use a generation AI to build a system that analyzes fluctuations in line status in real time and provides advance warning of predicted problems. For example, a warning is displayed before the line bandwidth becomes insufficient. This makes it possible to analyze fluctuations in line status in real time and provide advance warning of predicted problems.

[0084] When monitoring line status, the voice recognition unit can analyze line usage history and automatically adjust optimal communication settings. For example, the voice recognition unit adds a function whereby a generation AI analyzes line usage history and automatically adjusts optimal communication settings. For example, it sets the optimal bandwidth based on past usage patterns. The voice recognition unit can also develop an algorithm that analyzes line usage history and automatically adjusts optimal communication settings. For example, it sets the optimal communication settings based on peak usage. The voice recognition unit can also use generation AI to build a system that analyzes line usage history and automatically adjusts optimal communication settings. For example, it sets the optimal communication settings based on past data. This makes it possible to analyze line usage history and automatically adjust optimal communication settings.

[0085] When monitoring the line status, the speech recognition unit can use the emotion estimation function to measure the user's stress level and suggest appropriate countermeasures. For example, when the generation AI monitors the line status, the speech recognition unit can use the emotion estimation function to measure the user's stress level and suggest appropriate countermeasures. For example, it can suggest ways to relax if a line delay occurs. The speech recognition unit can also use the emotion estimation function to develop an algorithm that measures the user's stress level and suggests appropriate countermeasures. For example, it can display advice on stress reduction if the line quality deteriorates. The speech recognition unit can also use the generation AI to combine line status monitoring and emotion estimation to build a system that measures the user's stress level and suggests appropriate countermeasures. For example, it can suggest ways to relax if a line problem occurs. This makes it possible to measure the user's stress level and suggest appropriate countermeasures.

[0086] The voice recognition unit can apply the line status monitoring technology to all Internet connections to improve network quality within a home. The voice recognition unit can apply the line status monitoring technology to, for example, all Internet connections within a home to improve network quality. For example, the voice recognition unit can monitor the quality of Wi-Fi connections within a home in real time. The voice recognition unit can also apply the line status monitoring technology to all Internet connections to build a system that improves network quality within a home. For example, the voice recognition unit can monitor the quality of wired and wireless connections within a home. The voice recognition unit can also enhance the line status monitoring technology to develop a system that improves network quality within a home. For example, the voice recognition unit can analyze network traffic within a home and set optimal settings. This can improve network quality within a home.

[0087] The speech recognition unit can apply line status monitoring technology to a company's communications infrastructure to improve business efficiency. The speech recognition unit can apply line status monitoring technology to a company's communications infrastructure to improve business efficiency. For example, it can monitor network quality within a company in real time and respond immediately if a problem occurs. The speech recognition unit can also apply line status monitoring technology to a company's communications infrastructure to build a system that improves business efficiency. For example, it can analyze network traffic within a company and perform optimal settings. The speech recognition unit can also enhance line status monitoring technology to develop a system that improves the quality of a company's communications infrastructure. For example, it can monitor network quality within a company in real time and respond immediately if a problem occurs. This can improve the quality of a company's communications infrastructure and improve business efficiency.

[0088] The voice recognition unit can use the emotion estimation function to collect the user's emotional responses to the line status monitoring results and optimize the monitoring system. For example, the voice recognition unit can use the emotion estimation function to collect the user's emotional responses to the line status monitoring results and optimize the monitoring system based on that data. For example, it can adopt settings that result in a high percentage of positive emotional responses. The voice recognition unit can also develop a system that analyzes the user's emotional responses in real time and dynamically adjusts the monitoring system. For example, it can adjust the monitoring intensity according to the user's emotions. The voice recognition unit can also develop an algorithm that optimizes the line status monitoring system based on the emotion estimation data. For example, it can select optimal monitoring settings based on the user's emotion score. This allows the monitoring system to be optimized based on the user's emotional responses.

[0089] The speech recognition unit can provide real-time feedback to speakers to improve their pronunciation based on the analysis results of the other person's enunciation. For example, the speech recognition unit uses a generation AI to analyze the other person's enunciation and, based on the results, provides real-time feedback to the speaker to improve their enunciation. For example, it can suggest pronunciation practice methods. The speech recognition unit can also develop a system that provides specific points for improvement to speakers in real time based on the analysis results of enunciation. For example, it can instruct the speaker to emphasize the pronunciation of specific sounds. The speech recognition unit can also use a generation AI to provide real-time feedback to speakers based on the analysis results of the other person's enunciation and give advice to improve their enunciation. For example, it can teach them pronunciation tips. In this way, it is possible to provide real-time feedback to speakers to improve their enunciation based on the analysis results of the other person's enunciation.

[0090] When analyzing the other person's enunciation, the speech recognition unit can learn the speaker's pronunciation characteristics and perform individually optimized analysis. For example, when the generation AI analyzes the other person's enunciation, the speech recognition unit adds a function to learn the speaker's pronunciation characteristics and perform individually optimized analysis. For example, it learns the speaker's pronunciation patterns and improves analysis accuracy. The speech recognition unit can also learn the speaker's pronunciation characteristics and develop an algorithm to perform individually optimized enunciation analysis. For example, it performs analysis taking into account the speaker's pronunciation habits. The speech recognition unit can also use the generation AI to build a system that learns the speaker's pronunciation characteristics and performs individually optimized enunciation analysis. For example, it performs analysis taking into account the pronunciation patterns that differ for each speaker. This makes it possible to learn the speaker's pronunciation characteristics and perform individually optimized enunciation analysis.

[0091] When analyzing the other person's enunciation, the speech recognition unit can use the emotion estimation function to perform an analysis that takes into account the speaker's emotional state. For example, when the generation AI analyzes the other person's enunciation, the speech recognition unit uses the emotion estimation function to perform an analysis that takes into account the speaker's emotional state. For example, if the speaker is nervous, the analysis will take that influence into account. The speech recognition unit can also use the emotion estimation function to develop an algorithm that performs enunciation analysis that takes into account the speaker's emotional state. For example, if the speaker is relaxed, the analysis will take that influence into account. The speech recognition unit can also use the generation AI to build a system that performs enunciation analysis that takes into account the speaker's emotional state. For example, if the speaker is angry, the analysis will take that influence into account. This makes it possible to perform enunciation analysis that takes into account the speaker's emotional state.

[0092] The speech recognition unit can apply the articulation analysis technology to the fields of speech therapy or pronunciation instruction to provide effective training. The speech recognition unit can, for example, apply the articulation analysis technology to speech therapy to provide effective training. For example, it can identify pronunciation problems and suggest practice methods for improvement. The speech recognition unit can also apply the articulation analysis technology to the field of pronunciation instruction to build a system that provides effective training. For example, it can analyze pronunciation problems and provide guidance on specific points to improve. The speech recognition unit can also enhance the articulation analysis technology to develop a system that can be applied to the fields of speech therapy or pronunciation instruction. For example, it can identify pronunciation problems and suggest effective training methods. This allows the articulation analysis technology to be applied to the fields of speech therapy or pronunciation instruction to provide effective training.

[0093] The speech recognition unit can apply the articulation analysis technology to a voice assistant or a navigation system to provide an optimal response according to the user's pronunciation. The speech recognition unit can, for example, apply the articulation analysis technology to a voice assistant to provide an optimal response according to the user's pronunciation. For example, an accurate response can be provided even if the pronunciation is unclear. The speech recognition unit can also apply the articulation analysis technology to a navigation system to build a system that provides an optimal response according to the user's pronunciation. For example, accurate route guidance can be provided even if the pronunciation is unclear. The speech recognition unit can also enhance the articulation analysis technology to develop a system to be applied to a voice assistant or a navigation system. For example, an accurate response can be provided even if the pronunciation is unclear. This allows the articulation analysis technology to be applied to a voice assistant or a navigation system to provide an optimal response according to the user's pronunciation.

[0094] The speech recognition unit can use the emotion estimation function to collect the user's emotional reactions to the enunciation analysis results and optimize the analysis algorithm. The speech recognition unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the enunciation analysis results and optimizes the analysis algorithm based on that data. For example, it adopts settings that result in a high percentage of positive emotional reactions. The speech recognition unit can also develop a system that analyzes the user's emotional reactions in real time and dynamically adjusts the analysis algorithm. For example, it adjusts the accuracy of the analysis according to the user's emotions. The speech recognition unit can also develop an algorithm that optimizes the enunciation analysis algorithm based on the emotion estimation data. For example, it selects optimal analysis settings based on the user's emotion score. This allows the enunciation analysis algorithm to be optimized based on the user's emotional reactions.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The speech recognition unit can estimate the speaker's emotions and adjust the way text is displayed based on the estimated emotions. For example, if the speaker is nervous, the text display speed can be slowed down to improve readability. If the speaker is happy, the emotion can be visually conveyed by brightening the text color. Furthermore, if the speaker is angry, the intensity of the emotion can be emphasized by making the text bold. This makes it possible to display text according to the speaker's emotions, making visual information transmission more effective.

[0097] The speech recognition unit can estimate the speaker's emotions and adjust the way text is displayed based on the estimated emotions. For example, if the speaker is nervous, the text display speed can be slowed down to improve readability. If the speaker is happy, the emotion can be visually conveyed by brightening the text color. Furthermore, if the speaker is angry, the intensity of the emotion can be emphasized by making the text bold. This makes it possible to display text according to the speaker's emotions, making visual information transmission more effective.

[0098] The speech recognition unit can estimate the speaker's emotions and adjust the way text is displayed based on the estimated emotions. For example, if the speaker is nervous, the text display speed can be slowed down to improve readability. If the speaker is happy, the emotion can be visually conveyed by brightening the text color. Furthermore, if the speaker is angry, the intensity of the emotion can be emphasized by making the text bold. This makes it possible to display text according to the speaker's emotions, making visual information transmission more effective.

[0099] The speech recognition unit can estimate the speaker's emotions and adjust the way text is displayed based on the estimated emotions. For example, if the speaker is nervous, the text display speed can be slowed down to improve readability. If the speaker is happy, the emotion can be visually conveyed by brightening the text color. Furthermore, if the speaker is angry, the intensity of the emotion can be emphasized by making the text bold. This makes it possible to display text according to the speaker's emotions, making visual information transmission more effective.

[0100] The speech recognition unit can estimate the speaker's emotions and adjust the way text is displayed based on the estimated emotions. For example, if the speaker is nervous, the text display speed can be slowed down to improve readability. If the speaker is happy, the emotion can be visually conveyed by brightening the text color. Furthermore, if the speaker is angry, the intensity of the emotion can be emphasized by making the text bold. This makes it possible to display text according to the speaker's emotions, making visual information transmission more effective.

[0101] The speech recognition unit can automatically detect a speaker's accent or dialect and convert it into standard Japanese. For example, the speech recognition unit detects a speaker's accent by analyzing phonological patterns. For example, the speech recognition unit detects the phonological patterns of Kansai dialect and converts it into standard Japanese. The speech recognition unit can also detect dialects by analyzing regional pronunciation. For example, the speech recognition unit detects dialects from the Tohoku region and converts them into standard Japanese. The speech recognition unit can also convert dialects into standard Japanese using dictionary-based conversion technology. For example, the speech recognition unit converts dialect words into standard Japanese by looking up words in a dictionary. The speech recognition unit can also convert dialects into standard Japanese using a speech model. For example, the speech recognition unit trains a speech model to convert dialects into standard Japanese. This allows the speaker's accent or dialect to be converted into standard Japanese.

[0102] The speech recognition unit can understand the speaker's intention and convert ambiguous expressions into clear expressions. For example, the speech recognition unit understands the speaker's intention using natural language processing technology. For example, the speech recognition unit analyzes the context to understand the speaker's intention. The speech recognition unit can also convert ambiguous expressions into clear expressions using context analysis technology. For example, the speech recognition unit specifically converts "that" into "that product." The speech recognition unit can also convert ambiguous expressions into clear expressions using synonyms. For example, the speech recognition unit converts "that" into "that material." The speech recognition unit can also perform context-based interpretation to convert ambiguous expressions into clear expressions. For example, the speech recognition unit converts "this" into "this project." In this way, ambiguous expressions can be converted into clear expressions.

[0103] The speech recognition unit can be applied not only to telephone calls but also to video calls or online conferences, allowing for visual confirmation. For example, it can be applied to video calls using Zoom or Teams, converting the speaker's speech into text in real time and displaying it on the screen. For example, the speaker's remarks during a video call can be converted into text and displayed on the screen. The speech recognition unit can also convert speech during online conferences into text so that all participants can visually confirm the content. For example, the speaker's remarks can be converted into text and displayed on the screen during an online conference. The speech recognition unit can also enhance speech recognition technology in video calls and online conferences using Webex or Skype, converting the speaker's remarks into text in real time and displaying it on the screen. For example, the speaker's remarks can be converted into text and displayed on the screen during a video call or online conference. This allows for visual confirmation of the content even during video calls or online conferences.

[0104] The speech recognition unit can convert the content of lectures and seminars in educational settings into text in real time, allowing them to be visually confirmed. For example, university lectures can be converted into text in real time, allowing students to visually confirm the content. For example, the professor's remarks during a lecture can be converted into text and displayed. The speech recognition unit can also convert the content of a business seminar into text in real time, allowing participants to visually confirm the content. For example, the lecturer's remarks during a seminar can be converted into text and displayed. The speech recognition unit can also convert the content of lectures and seminars on online education platforms into text in real time, allowing them to be visually confirmed. For example, the lecturer's remarks during an online lecture can be converted into text and displayed. This allows the content to be visually confirmed even in educational settings and seminars.

[0105] The speech recognition unit uses the emotion estimation function to provide real-time feedback according to the speaker's emotion, thereby improving the quality of communication. For example, the emotion estimation function is used to analyze the speaker's emotion in real time, and provide feedback according to the emotion. For example, if the speaker is nervous, a message urging the speaker to relax is displayed. The speech recognition unit also develops a system that analyzes the speaker's emotion and provides feedback according to the emotion. For example, if the speaker is happy, positive feedback is displayed. The speech recognition unit also uses the emotion estimation function to provide real-time feedback according to the speaker's emotion, thereby improving the quality of communication. For example, if the speaker is angry, a message urging the speaker to stay calm is displayed. This allows for providing feedback according to the speaker's emotion, thereby improving the quality of communication.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The speech recognition unit analyzes the phone call in real time. For example, the speech recognition unit analyzes the voice data using deep learning technology or HMM (Hidden Markov Model), and analyzes the voice data with a delay of milliseconds. Step 2: The text generator converts the speech analyzed by the speech recognizer into text. For example, the text generator converts the speech data into text while maintaining grammatical consistency and inserts punctuation marks appropriately to generate text with high accuracy. Step 3: The display unit displays the text generated by the text generator on the television screen. For example, the display unit adjusts the font size, display position, and color of the text to improve visibility.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 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 recognition unit that analyzes the voice of the phone in real time, a text generation unit that converts the speech analyzed by the speech recognition unit into text; a display unit that displays the text generated by the text generation unit on a television screen. A system characterized by:

2. The voice recognition unit Filters out ambient noise from audio data to generate clear audio data 2. The system of claim 1.

3. The voice recognition unit Monitors the quality of telephone lines and displays a warning if the line is poor 2. The system of claim 1.

4. The voice recognition unit Analyze the other person's pronunciation and display the fact if the pronunciation is poor 2. The system of claim 1.

5. The voice recognition unit The speaker's emotions are estimated, and the color and font of the text are changed according to the emotion, visually conveying the nuances of the emotion.

2. The system of claim 1.

6. The voice recognition unit Automatically detects the speaker's accent or dialect and converts it into standard Japanese 2. The system of claim 1.

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