System

Abstract

An object of the system according to the embodiment is to realize smooth communication between a wax and a hearing person.SOLUTION: A system according to an embodiment includes a sign language recognizer, a speech converter, a speech recognizer, and a sign language video generator. The sign language recognizer may recognize a sign language using a camera device. The speech conversion unit converts the sign language recognized by the sign language recognition unit into speech. The voice recognition unit recognizes a voice of a conversation partner. The sign language video generator converts the speech recognized by the speech recognizer into a sign language video.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult for deaf people and hearing people to communicate smoothly.

[0005] The system according to the embodiment aims to realize smooth communication between deaf people and people with normal hearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a sign language recognition unit, a voice conversion unit, a voice recognition unit, and a sign language video generation unit. The sign language recognition unit recognizes sign language using a camera device. The voice conversion unit converts the sign language recognized by the sign language recognition unit into voice. The voice recognition unit recognizes the voice of the conversation partner. The sign language video generation unit converts the voice recognized by the voice recognition unit into a sign language video. [Effects of the Invention]

[0007] The system according to the embodiment can realize smooth communication between deaf people and people with normal hearing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A communication system according to an embodiment of the present invention is a system that realizes smooth communication between deaf people who use sign language and hearing people who use speech. This system trains an LLM to learn sign language data, recognizes sign language using a tablet's camera device, converts it into speech, and transmits it to the other person. It also converts the speech of the other person into a sign language image so that the deaf person can confirm it. This allows the communication system to realize smooth communication between deaf people and hearing people.

[0029] A communication system according to an embodiment includes a sign language recognition unit, a speech conversion unit, a speech recognition unit, and a sign language video generation unit. The sign language recognition unit recognizes sign language using a camera device. For example, the sign language recognition unit recognizes sign language in real time using a tablet's built-in camera. The sign language recognition unit can also recognize sign language using an externally connected camera. The sign language recognition unit analyzes sign language movements using image processing technology and recognizes the content of the sign language. For example, the sign language recognition unit analyzes sign language movements using a machine learning model and recognizes the content of the sign language. The speech conversion unit converts the sign language recognized by the sign language recognition unit into speech. For example, the speech conversion unit converts sign language into speech using speech synthesis technology. The speech conversion unit can also convert sign language into speech using natural language processing technology. The speech conversion unit analyzes the content of the sign language and generates appropriate speech. For example, the speech conversion unit analyzes the content of the sign language and generates appropriate speech. The speech recognition unit recognizes the speech of the conversation partner. For example, the speech recognition unit recognizes the speech of the conversation partner using a speech recognition engine. The speech recognition unit can also recognize the speech of the interlocutor using a machine learning model. Furthermore, the speech recognition unit analyzes the content of the speech and generates an appropriate sign language video. For example, the speech recognition unit analyzes the content of the speech and generates an appropriate sign language video. The sign language video generation unit converts the speech recognized by the speech recognition unit into a sign language video. For example, the sign language video generation unit converts the speech into a sign language video using animation generation technology. Furthermore, the sign language video generation unit can also convert the speech into a sign language video using a sign language database. Furthermore, the sign language video generation unit analyzes the content of the speech and generates an appropriate sign language video. For example, the sign language video generation unit analyzes the content of the speech and generates an appropriate sign language video. This enables the communication system according to the embodiment to realize two-way real-time communication between sign language and speech. For example, the output unit displays the generated sign language video on a tablet screen. Furthermore, the output unit can output the generated speech from a speaker. Furthermore, the output unit can display the generated sign language video on an external display.

[0030] The sign language recognition unit can use a dataset that includes not only sign language actions but also facial expressions and body movements when performing the sign language. The sign language recognition unit uses, for example, a dataset that includes not only sign language actions but also facial expressions and body movements when performing the sign language. For example, in addition to sign language actions, facial expression data such as eyebrow movements and mouth shapes is collected and trained by the LLM. The sign language recognition unit also uses a dataset that includes body movements when performing the sign language. For example, body movement data such as shoulder movements and body tilt is collected and trained by the LLM. Furthermore, the sign language recognition unit recognizes sign language actions, facial expressions, and body movements in an integrated manner. For example, by recognizing sign language actions, facial expressions, and body movements in an integrated manner, more natural sign language recognition is achieved. In this way, by using a dataset that includes not only sign language actions but also facial expressions and body movements when performing the sign language, more natural sign language recognition can be achieved.

[0031] The sign language recognition unit can integrate and train sign language data from different regions, taking into account regional differences and dialects in sign language. For example, the sign language recognition unit collects sign language data from different regions, taking into account regional differences and dialects in sign language. For example, data from Japanese Sign Language and American Sign Language is integrated and trained on the LLM. The sign language recognition unit also uses datasets that take into account regional differences and dialects in sign language. For example, a dataset containing the characteristics of sign language used in a specific region is collected and trained on the LLM. Furthermore, the sign language recognition unit integrates regional differences and dialects in sign language for recognition. For example, by integrating regional differences and dialects in sign language for recognition, more accurate sign language recognition is achieved. This enables sign language recognition that takes into account regional differences and dialects in sign language.

[0032] The sign language recognition unit can recognize non-verbal communication, including gestures and body language other than sign language. The sign language recognition unit, for example, includes gestures and body language other than sign language. For example, gestures of greeting and gratitude are collected and trained in the LLM. The sign language recognition unit also recognizes non-verbal communication. For example, it recognizes gestures such as hand movements and body movements and analyzes non-verbal communication. Furthermore, the sign language recognition unit integrates and recognizes gestures and body language other than sign language. For example, it integrates and recognizes sign language actions, gestures, and body language to realize a wider range of non-verbal communication. This makes it possible to recognize non-verbal communication, including gestures and body language other than sign language, thereby realizing a wider range of non-verbal communication.

[0033] The sign language recognition unit can accommodate a variety of users, including sign language data from different age groups and genders. The sign language recognition unit, for example, collects sign language data from different age groups. For example, sign language data from children to the elderly is collected and trained by the LLM. The sign language recognition unit also collects sign language data from different genders. For example, sign language data from men and women is collected and trained by the LLM. Furthermore, the sign language recognition unit integrates and recognizes sign language data from different age groups and genders. For example, sign language data from different age groups and genders is integrated and recognized to accommodate a variety of users. This allows the system to accommodate a wider variety of users by including sign language data from different age groups and genders.

[0034] The sign language recognition unit takes into account not only hand movements but also hand position and hand speed, thereby achieving more accurate sign language recognition. The sign language recognition unit, for example, takes into account not only hand movements but also hand position and speed. For example, hand position data is collected and trained by an LLM. The sign language recognition unit also collects hand speed data. For example, hand movement speed data is collected and trained by an LLM. Furthermore, the sign language recognition unit recognizes hand movements, position, and speed in an integrated manner. For example, hand movements, position, and speed are recognized in an integrated manner, thereby achieving more accurate sign language recognition. This allows for more accurate sign language recognition by taking into account not only hand movements but also hand position and speed.

[0035] The sign language recognition unit can take into account the environment in which the sign language is spoken and apply the optimal recognition algorithm depending on the background, lighting, and camera angle. The sign language recognition unit, for example, takes into account the environment in which the sign language is spoken. For example, it collects background data and trains the LLM. The sign language recognition unit also collects lighting data. For example, it collects data on the brightness and angle of lighting and trains the LLM. The sign language recognition unit also collects camera angle data. For example, it collects data on the installation position and orientation of the camera and trains the LLM. In this way, by taking into account the environment in which the sign language is spoken and applying the optimal recognition algorithm depending on the background, lighting, and camera angle, more accurate sign language recognition can be achieved.

[0036] The sign language recognition unit can improve recognition accuracy by integrating sign language videos from different angles using multiple camera devices. The sign language recognition unit, for example, uses multiple camera devices. For example, sign language videos from different angles are collected and trained by an LLM. The sign language recognition unit also integrates sign language videos from different angles. For example, sign language videos collected from multiple camera devices are integrated to improve recognition accuracy. Furthermore, the sign language recognition unit analyzes sign language movements using multiple camera devices. For example, sign language videos from different angles are analyzed and sign language movements are recognized. This allows sign language videos from different angles to be integrated using multiple camera devices to improve recognition accuracy.

[0037] The sign language recognition unit filters background sounds and environmental sounds when signing, thereby minimizing the influence of noise. The sign language recognition unit, for example, filters background sounds and environmental sounds. For example, it removes background sounds using noise canceling technology. The sign language recognition unit also filters environmental sounds. For example, it removes environmental sounds such as wind noise and traffic noise. Furthermore, the sign language recognition unit minimizes the influence of noise. For example, it removes noise from audio signals and video signals. In this way, it is possible to filter background sounds and environmental sounds when signing, thereby minimizing the influence of noise.

[0038] The voice recognition unit takes into account the intonation and rhythm of the voice, thereby generating a more natural sign language video. The voice recognition unit, for example, takes into account the intonation of the voice. For example, it analyzes the pitch and strength of the voice and generates a sign language video accordingly. The voice recognition unit also takes into account the rhythm of the voice. For example, it analyzes the tempo and interval of the voice and generates a sign language video accordingly. Furthermore, the voice recognition unit generates a sign language video by integrating the intonation and rhythm of the voice. For example, it generates a sign language video by integrating the pitch, strength, tempo, and interval of the voice, thereby achieving a more natural sign language video. In this way, it is possible to generate a more natural sign language video by taking into account the intonation and rhythm of the voice.

[0039] The speech recognition unit can select an appropriate sign language expression by taking into account the context and meaning of the speech. The speech recognition unit, for example, takes into account the context of the speech. For example, it analyzes the context of the speech and selects an appropriate sign language expression. The speech recognition unit also takes into account the meaning of the speech. For example, it analyzes the meaning of words and sentences and selects an appropriate sign language expression. Furthermore, the speech recognition unit selects a sign language expression by integrating the context and meaning of the speech. For example, it selects a sign language expression by integrating the context of the speech, the meaning of words, and the meaning of sentences, thereby realizing a more appropriate sign language expression. This makes it possible to select an appropriate sign language expression by taking into account the context and meaning of the speech.

[0040] The speech recognition unit adds a function that allows selection of different sign language expressions, making it possible to provide sign language videos that suit the user's preferences. The speech recognition unit adds, for example, a function that allows selection of different sign language expressions. For example, sign language videos that suit regional differences or dialects are generated. The speech recognition unit also provides sign language videos that suit the user's preferences. For example, sign language videos are generated based on the sign language expression selected by the user. Furthermore, the speech recognition unit generates sign language videos by integrating different sign language expressions. For example, sign language videos are generated by integrating regional differences or dialects, making it possible to achieve a wider variety of sign language expressions. This adds a function that allows selection of different sign language expressions, making it possible to provide sign language videos that suit the user's preferences.

[0041] The voice recognition unit can visualize non-verbal communication, including gestures and body language other than sign language, as a sign language video. The voice recognition unit also includes, for example, gestures and body language other than sign language. For example, it visualizes gestures of greeting and gratitude. The voice recognition unit also visualizes non-verbal communication as a sign language video. For example, it visualizes gestures such as hand movements and body movements as a sign language video. Furthermore, the voice recognition unit generates a sign language video by integrating gestures and body language other than sign language. For example, it integrates sign language actions, gestures, and body language to generate a sign language video, thereby realizing a wider range of non-verbal communication. In this way, by visualizing non-verbal communication, including gestures and body language other than sign language, as a sign language video, it is possible to realize a wider range of non-verbal communication.

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

[0043] The communication system may further include a translation unit. The translation unit has the function of translating sign language and speech into other languages. For example, it can translate sign language into English and output it as English speech. It can also translate the English speech of the person speaking into Japanese Sign Language and display it as a sign language video. Furthermore, the translation unit can support multiple languages. For example, it can translate sign language into French and Spanish and output it as speech in each language. This can support communication between users who speak different languages.

[0044] The communication system may further include a user authentication unit. The user authentication unit has a function of restricting access to the system using facial recognition or fingerprint authentication of the user. For example, facial recognition technology may be used to recognize the user's face, allowing only authenticated users to use the system. Alternatively, fingerprint authentication technology may be used to recognize the user's fingerprint, allowing only authenticated users to use the system. Furthermore, the user authentication unit may combine multiple authentication methods to enhance security. For example, higher security may be achieved by combining facial recognition and fingerprint authentication.

[0045] The communication system may further include a health management unit that monitors the user's health condition. The health management unit has the function of measuring the user's heart rate and blood pressure and monitoring the user's health condition in real time. For example, it may measure the user's heart rate using a heart rate sensor and issue an alert if an abnormality is detected. It may also measure the user's blood pressure using a blood pressure sensor and issue an alert if an abnormality is detected. Furthermore, the health management unit may record the measurement data and track changes in the user's health condition. This allows the user's health condition to be monitored and a prompt response to be taken if an abnormality is detected.

[0046] The communication system may further include an activity recording unit that records and analyzes the user's activity history. The activity recording unit has a function of recording and analyzing the user's sign language and speech usage history. For example, it can record and analyze what sign language the user frequently uses. It can also record and analyze what speech the user frequently uses. Furthermore, the activity recording unit can analyze the user's communication patterns based on the recorded data. For example, it can analyze the frequency and timing of the user's use of specific sign language or speech and suggest areas for improving communication. In this way, by recording and analyzing the user's activity history, it is possible to support more effective communication.

[0047] The communication system can further include a learning management unit that manages the user's learning progress. The learning management unit has the function of managing the user's sign language and speech learning progress. For example, it can record a list of sign languages ​​that the user has learned and display the progress. It can also record a list of speech sounds that the user has learned and display the progress. Furthermore, the learning management unit can provide feedback according to the user's learning progress. For example, if the user has mastered a specific sign language or speech sound, it can display a message of praise. This makes it possible to manage the user's learning progress and improve their motivation.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The sign language recognition unit recognizes sign language using a camera device, such as a tablet's built-in camera or an externally connected camera, and uses image processing technology and machine learning models to analyze sign language movements and recognize the content of the sign language. Step 2: The speech conversion unit converts the sign language recognized by the sign language recognition unit into speech. For example, it converts the sign language into speech using speech synthesis technology or natural language processing technology, analyzes the content of the sign language, and generates appropriate speech. Step 3: The speech recognition unit recognizes the speech of the person being spoken to. For example, it uses a speech recognition engine or a machine learning model to recognize the speech of the person being spoken to, analyzes the content of the speech, and generates an appropriate sign language image. Step 4: The sign language image generator converts the speech recognized by the speech recognition unit into a sign language image. For example, it converts the speech into a sign language image using animation generation technology or a sign language database, analyzes the content of the speech, and generates an appropriate sign language image.

[0050] (Example 2) A communication system according to an embodiment of the present invention is a system that realizes smooth communication between deaf people who use sign language and hearing people who use speech. This system trains an LLM to learn sign language data, recognizes sign language using a tablet's camera device, converts it into speech, and transmits it to the other person. It also converts the speech of the other person into a sign language image so that the deaf person can confirm it. This allows the communication system to realize smooth communication between deaf people and hearing people.

[0051] A communication system according to an embodiment includes a sign language recognition unit, a speech conversion unit, a speech recognition unit, and a sign language video generation unit. The sign language recognition unit recognizes sign language using a camera device. For example, the sign language recognition unit recognizes sign language in real time using a tablet's built-in camera. The sign language recognition unit can also recognize sign language using an externally connected camera. The sign language recognition unit analyzes sign language movements using image processing technology and recognizes the content of the sign language. For example, the sign language recognition unit analyzes sign language movements using a machine learning model and recognizes the content of the sign language. The speech conversion unit converts the sign language recognized by the sign language recognition unit into speech. For example, the speech conversion unit converts sign language into speech using speech synthesis technology. The speech conversion unit can also convert sign language into speech using natural language processing technology. The speech conversion unit analyzes the content of the sign language and generates appropriate speech. For example, the speech conversion unit analyzes the content of the sign language and generates appropriate speech. The speech recognition unit recognizes the speech of the conversation partner. For example, the speech recognition unit recognizes the speech of the conversation partner using a speech recognition engine. The speech recognition unit can also recognize the speech of the interlocutor using a machine learning model. Furthermore, the speech recognition unit analyzes the content of the speech and generates an appropriate sign language video. For example, the speech recognition unit analyzes the content of the speech and generates an appropriate sign language video. The sign language video generation unit converts the speech recognized by the speech recognition unit into a sign language video. For example, the sign language video generation unit converts the speech into a sign language video using animation generation technology. Furthermore, the sign language video generation unit can also convert the speech into a sign language video using a sign language database. Furthermore, the sign language video generation unit analyzes the content of the speech and generates an appropriate sign language video. For example, the sign language video generation unit analyzes the content of the speech and generates an appropriate sign language video. This enables the communication system according to the embodiment to realize two-way real-time communication between sign language and speech. For example, the output unit displays the generated sign language video on a tablet screen. Furthermore, the output unit can output the generated speech from a speaker. Furthermore, the output unit can display the generated sign language video on an external display.

[0052] The sign language recognition unit can use a dataset that includes not only sign language actions but also facial expressions and body movements when performing the sign language. The sign language recognition unit uses, for example, a dataset that includes not only sign language actions but also facial expressions and body movements when performing the sign language. For example, in addition to sign language actions, facial expression data such as eyebrow movements and mouth shapes is collected and trained by the LLM. The sign language recognition unit also uses a dataset that includes body movements when performing the sign language. For example, body movement data such as shoulder movements and body tilt is collected and trained by the LLM. Furthermore, the sign language recognition unit recognizes sign language actions, facial expressions, and body movements in an integrated manner. For example, by recognizing sign language actions, facial expressions, and body movements in an integrated manner, more natural sign language recognition is achieved. In this way, by using a dataset that includes not only sign language actions but also facial expressions and body movements when performing the sign language, more natural sign language recognition can be achieved.

[0053] The sign language recognition unit can integrate and train sign language data from different regions, taking into account regional differences and dialects in sign language. For example, the sign language recognition unit collects sign language data from different regions, taking into account regional differences and dialects in sign language. For example, data from Japanese Sign Language and American Sign Language is integrated and trained on the LLM. The sign language recognition unit also uses datasets that take into account regional differences and dialects in sign language. For example, a dataset containing the characteristics of sign language used in a specific region is collected and trained on the LLM. Furthermore, the sign language recognition unit integrates regional differences and dialects in sign language for recognition. For example, by integrating regional differences and dialects in sign language for recognition, more accurate sign language recognition is achieved. This enables sign language recognition that takes into account regional differences and dialects in sign language.

[0054] The sign language recognition unit can estimate emotions when using sign language using an emotion estimation function and perform voice conversion according to the emotions. The sign language recognition unit, for example, estimates emotions when using sign language using the emotion estimation function. For example, it analyzes sign language movements and facial expression data and calculates an emotion score. The sign language recognition unit also performs voice conversion according to the emotions. For example, it adjusts the intonation and tone of the voice based on the emotion score. Furthermore, the sign language recognition unit achieves voice conversion that is rich in emotion. For example, it adjusts the intonation and tone of the voice based on the emotion score to generate voice that is rich in emotion. In this way, by using the emotion estimation function to estimate emotions when using sign language and performing voice conversion according to the emotions, it is possible to achieve more emotional communication.

[0055] The sign language recognition unit can recognize non-verbal communication, including gestures and body language other than sign language. The sign language recognition unit, for example, includes gestures and body language other than sign language. For example, gestures of greeting and gratitude are collected and trained in the LLM. The sign language recognition unit also recognizes non-verbal communication. For example, it recognizes gestures such as hand movements and body movements and analyzes non-verbal communication. Furthermore, the sign language recognition unit integrates and recognizes gestures and body language other than sign language. For example, it integrates and recognizes sign language actions, gestures, and body language to realize a wider range of non-verbal communication. This makes it possible to recognize non-verbal communication, including gestures and body language other than sign language, thereby realizing a wider range of non-verbal communication.

[0056] The sign language recognition unit can accommodate a variety of users, including sign language data from different age groups and genders. The sign language recognition unit, for example, collects sign language data from different age groups. For example, sign language data from children to the elderly is collected and trained by the LLM. The sign language recognition unit also collects sign language data from different genders. For example, sign language data from men and women is collected and trained by the LLM. Furthermore, the sign language recognition unit integrates and recognizes sign language data from different age groups and genders. For example, sign language data from different age groups and genders is integrated and recognized to accommodate a variety of users. This allows the system to accommodate a wider variety of users by including sign language data from different age groups and genders.

[0057] The sign language recognition unit uses the emotion estimation function to provide feedback on the user's emotion in real time when learning sign language data, thereby improving the accuracy of learning. The sign language recognition unit, for example, uses the emotion estimation function to provide feedback on the user's emotion in real time when learning sign language data. For example, it analyzes sign language movements and facial expression data to calculate an emotion score. The sign language recognition unit also improves the learning accuracy based on the emotion score. For example, it adjusts learning data based on the emotion score to improve the learning accuracy. Furthermore, the sign language recognition unit realizes learning of emotion-rich sign language data. For example, it adjusts learning data based on the emotion score to learn emotion-rich sign language data. This allows the emotion estimation function to provide feedback on the user's emotion in real time when learning sign language data, thereby improving the accuracy of learning.

[0058] The sign language recognition unit takes into account not only hand movements but also hand position and hand speed, thereby achieving more accurate sign language recognition. The sign language recognition unit, for example, takes into account not only hand movements but also hand position and speed. For example, hand position data is collected and trained by an LLM. The sign language recognition unit also collects hand speed data. For example, hand movement speed data is collected and trained by an LLM. Furthermore, the sign language recognition unit recognizes hand movements, position, and speed in an integrated manner. For example, hand movements, position, and speed are recognized in an integrated manner, thereby achieving more accurate sign language recognition. This allows for more accurate sign language recognition by taking into account not only hand movements but also hand position and speed.

[0059] The sign language recognition unit can take into account the environment in which the sign language is spoken and apply the optimal recognition algorithm depending on the background, lighting, and camera angle. The sign language recognition unit, for example, takes into account the environment in which the sign language is spoken. For example, it collects background data and trains the LLM. The sign language recognition unit also collects lighting data. For example, it collects data on the brightness and angle of lighting and trains the LLM. The sign language recognition unit also collects camera angle data. For example, it collects data on the installation position and orientation of the camera and trains the LLM. In this way, by taking into account the environment in which the sign language is spoken and applying the optimal recognition algorithm depending on the background, lighting, and camera angle, more accurate sign language recognition can be achieved.

[0060] The sign language recognition unit can use the emotion estimation function to estimate the user's emotion when recognizing sign language and perform voice conversion according to that emotion. The sign language recognition unit, for example, uses the emotion estimation function to estimate the user's emotion when recognizing sign language. For example, it analyzes sign language movements and facial expression data and calculates an emotion score. The sign language recognition unit also performs voice conversion according to the emotion. For example, it adjusts the intonation and tone of the voice based on the emotion score. Furthermore, the sign language recognition unit achieves voice conversion that is rich in emotion. For example, it adjusts the intonation and tone of the voice based on the emotion score to generate voice that is rich in emotion. In this way, it is possible to achieve a more emotional voice output by using the emotion estimation function to estimate the user's emotion when recognizing sign language and performing voice conversion according to that emotion.

[0061] The sign language recognition unit can improve recognition accuracy by integrating sign language videos from different angles using multiple camera devices. The sign language recognition unit, for example, uses multiple camera devices. For example, sign language videos from different angles are collected and trained by an LLM. The sign language recognition unit also integrates sign language videos from different angles. For example, sign language videos collected from multiple camera devices are integrated to improve recognition accuracy. Furthermore, the sign language recognition unit analyzes sign language movements using multiple camera devices. For example, sign language videos from different angles are analyzed and sign language movements are recognized. This allows sign language videos from different angles to be integrated using multiple camera devices to improve recognition accuracy.

[0062] The sign language recognition unit filters background sounds and environmental sounds when signing, thereby minimizing the influence of noise. The sign language recognition unit, for example, filters background sounds and environmental sounds. For example, it removes background sounds using noise canceling technology. The sign language recognition unit also filters environmental sounds. For example, it removes environmental sounds such as wind noise and traffic noise. Furthermore, the sign language recognition unit minimizes the influence of noise. For example, it removes noise from audio signals and video signals. In this way, it is possible to filter background sounds and environmental sounds when signing, thereby minimizing the influence of noise.

[0063] The sign language recognition unit can improve recognition accuracy by using the emotion estimation function to provide feedback on the user's emotion in real time when recognizing sign language. The sign language recognition unit, for example, uses the emotion estimation function to provide feedback on the user's emotion in real time when recognizing sign language. For example, the sign language recognition unit analyzes sign language movements and facial expression data to calculate an emotion score. The sign language recognition unit also improves recognition accuracy based on the emotion score. For example, the recognition algorithm is adjusted based on the emotion score to improve recognition accuracy. Furthermore, the sign language recognition unit achieves emotion-rich sign language recognition. For example, the recognition algorithm is adjusted based on the emotion score to achieve emotion-rich sign language recognition. This allows the emotion estimation function to provide feedback on the user's emotion in real time when recognizing sign language, improving recognition accuracy.

[0064] The voice recognition unit takes into account the intonation and rhythm of the voice, thereby generating a more natural sign language video. The voice recognition unit, for example, takes into account the intonation of the voice. For example, it analyzes the pitch and strength of the voice and generates a sign language video accordingly. The voice recognition unit also takes into account the rhythm of the voice. For example, it analyzes the tempo and interval of the voice and generates a sign language video accordingly. Furthermore, the voice recognition unit generates a sign language video by integrating the intonation and rhythm of the voice. For example, it generates a sign language video by integrating the pitch, strength, tempo, and interval of the voice, thereby achieving a more natural sign language video. In this way, it is possible to generate a more natural sign language video by taking into account the intonation and rhythm of the voice.

[0065] The speech recognition unit can select an appropriate sign language expression by taking into account the context and meaning of the speech. The speech recognition unit, for example, takes into account the context of the speech. For example, it analyzes the context of the speech and selects an appropriate sign language expression. The speech recognition unit also takes into account the meaning of the speech. For example, it analyzes the meaning of words and sentences and selects an appropriate sign language expression. Furthermore, the speech recognition unit selects a sign language expression by integrating the context and meaning of the speech. For example, it selects a sign language expression by integrating the context of the speech, the meaning of words, and the meaning of sentences, thereby realizing a more appropriate sign language expression. This makes it possible to select an appropriate sign language expression by taking into account the context and meaning of the speech.

[0066] The speech recognition unit can use the emotion estimation function to estimate the emotion of a speaker when converting speech into a sign language video, and generate a sign language video corresponding to that emotion. The speech recognition unit, for example, uses the emotion estimation function to estimate the emotion of a speaker when converting speech into a sign language video. For example, the speech recognition unit analyzes the tone and rhythm of the speech and calculates an emotion score. The speech recognition unit also generates a sign language video corresponding to the emotion. For example, the speech recognition unit adjusts the intonation and movements of the sign language video based on the emotion score. Furthermore, the speech recognition unit realizes an emotionally rich sign language video. For example, the speech recognition unit adjusts the intonation and movements of the sign language video based on the emotion score, and generates an emotionally rich sign language video. In this way, by using the emotion estimation function to estimate the emotion of a speaker when converting speech into a sign language video and generating a sign language video corresponding to that emotion, it is possible to realize a more emotionally rich sign language video.

[0067] The speech recognition unit adds a function that allows selection of different sign language expressions, making it possible to provide sign language videos that suit the user's preferences. The speech recognition unit adds, for example, a function that allows selection of different sign language expressions. For example, sign language videos that suit regional differences or dialects are generated. The speech recognition unit also provides sign language videos that suit the user's preferences. For example, sign language videos are generated based on the sign language expression selected by the user. Furthermore, the speech recognition unit generates sign language videos by integrating different sign language expressions. For example, sign language videos are generated by integrating regional differences or dialects, making it possible to achieve a wider variety of sign language expressions. This adds a function that allows selection of different sign language expressions, making it possible to provide sign language videos that suit the user's preferences.

[0068] The voice recognition unit can visualize non-verbal communication, including gestures and body language other than sign language, as a sign language video. The voice recognition unit also includes, for example, gestures and body language other than sign language. For example, it visualizes gestures of greeting and gratitude. The voice recognition unit also visualizes non-verbal communication as a sign language video. For example, it visualizes gestures such as hand movements and body movements as a sign language video. Furthermore, the voice recognition unit generates a sign language video by integrating gestures and body language other than sign language. For example, it integrates sign language actions, gestures, and body language to generate a sign language video, thereby realizing a wider range of non-verbal communication. In this way, by visualizing non-verbal communication, including gestures and body language other than sign language, as a sign language video, it is possible to realize a wider range of non-verbal communication.

[0069] The speech recognition unit uses the emotion estimation function to provide feedback on the speaker's emotion in real time when converting speech into sign language video, thereby improving the accuracy of the sign language video. The speech recognition unit, for example, uses the emotion estimation function to provide feedback on the speaker's emotion in real time when converting speech into sign language video. For example, the speech recognition unit analyzes the tone and rhythm of the speech and calculates an emotion score. The speech recognition unit also improves the accuracy of the sign language video based on the emotion score. For example, the speech recognition unit adjusts the intonation and movements of the sign language video based on the emotion score. Furthermore, the speech recognition unit realizes emotionally rich sign language video. For example, the speech recognition unit adjusts the intonation and movements of the sign language video based on the emotion score to generate emotionally rich sign language video. This allows the emotion estimation function to provide feedback on the speaker's emotion in real time when converting speech into sign language video, thereby improving the accuracy of the sign language video.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The communication system may further include a translation unit. The translation unit has the function of translating sign language and speech into other languages. For example, it can translate sign language into English and output it as English speech. It can also translate the English speech of the person speaking into Japanese Sign Language and display it as a sign language video. Furthermore, the translation unit can support multiple languages. For example, it can translate sign language into French and Spanish and output it as speech in each language. This can support communication between users who speak different languages.

[0072] The communication system may further include a user authentication unit. The user authentication unit has a function of restricting access to the system using facial recognition or fingerprint authentication of the user. For example, facial recognition technology may be used to recognize the user's face, allowing only authenticated users to use the system. Alternatively, fingerprint authentication technology may be used to recognize the user's fingerprint, allowing only authenticated users to use the system. Furthermore, the user authentication unit may combine multiple authentication methods to enhance security. For example, higher security may be achieved by combining facial recognition and fingerprint authentication.

[0073] The communication system may further include a health management unit that monitors the user's health condition. The health management unit has the function of measuring the user's heart rate and blood pressure and monitoring the user's health condition in real time. For example, it may measure the user's heart rate using a heart rate sensor and issue an alert if an abnormality is detected. It may also measure the user's blood pressure using a blood pressure sensor and issue an alert if an abnormality is detected. Furthermore, the health management unit may record the measurement data and track changes in the user's health condition. This allows the user's health condition to be monitored and a prompt response to be taken if an abnormality is detected.

[0074] The communication system may further include a feedback unit that estimates the user's emotions and provides feedback according to the emotions. The feedback unit has a function of estimating the user's emotions and providing feedback according to the emotions. For example, if the user is feeling stressed, it may provide advice on how to relax. Also, if the user is happy, it may display a message to share the user's emotions. Furthermore, the feedback unit may provide music or images according to the user's emotions. For example, if the user wants to relax, it may provide relaxing music or images. This allows the system to provide feedback according to the user's emotions and improve user satisfaction.

[0075] The communication system may further include an avatar generation unit that estimates the user's emotions and generates an avatar according to the emotions. The avatar generation unit has the function of estimating the user's emotions and generating an avatar according to the emotions. For example, if the user is happy, it can generate an avatar with a smiling expression. Also, if the user is sad, it can generate an avatar with a sad expression. Furthermore, the avatar generation unit can generate avatar movements according to the user's emotions. For example, if the user is excited, it can generate an avatar with a waving movement. In this way, an avatar according to the user's emotions can be generated, enabling more emotional communication.

[0076] The communication system can further include a stamp providing unit that estimates the user's emotions and provides emotion stamps according to the emotions. The stamp providing unit has a function of estimating the user's emotions and providing emotion stamps according to the emotions. For example, if the user is happy, it can provide a smiling stamp. If the user is sad, it can provide a tearful stamp. Furthermore, the stamp providing unit can provide a variation of stamps according to the user's emotions. For example, if the user is excited, it can provide a stamp that expresses excitement. This allows for providing emotion stamps according to the user's emotions, thereby realizing more emotionally rich communication.

[0077] The communication system may further include a reminder unit that estimates the user's emotions and provides a reminder according to the emotions. The reminder unit has a function of estimating the user's emotions and providing a reminder according to the emotions. For example, if the user is feeling stressed, the reminder unit can remind the user to take a break. Also, if the user is relaxed, the reminder unit can provide advice on how to maintain that state. Furthermore, the reminder unit can adjust the content of the reminder according to the user's emotions. For example, if the user is tired, the reminder unit can remind the user to go to bed early. In this way, the reminder unit can provide a reminder according to the user's emotions and support the user's health management.

[0078] The communication system may further include an exercise suggestion unit that estimates the user's emotions and suggests exercises according to the emotions. The exercise suggestion unit has a function of estimating the user's emotions and suggesting exercises according to the emotions. For example, if the user is feeling stressed, it may suggest a relaxing yoga exercise. If the user is energetic, it may suggest running or dancing exercises. Furthermore, the exercise suggestion unit may adjust the intensity of the exercise according to the user's emotions. For example, if the user is tired, it may suggest light stretching. This makes it possible to suggest exercises according to the user's emotions and support the user's health management.

[0079] The communication system may further include an activity recording unit that records and analyzes the user's activity history. The activity recording unit has a function of recording and analyzing the user's sign language and speech usage history. For example, it can record and analyze what sign language the user frequently uses. It can also record and analyze what speech the user frequently uses. Furthermore, the activity recording unit can analyze the user's communication patterns based on the recorded data. For example, it can analyze the frequency and timing of the user's use of specific sign language or speech and suggest areas for improving communication. In this way, by recording and analyzing the user's activity history, it is possible to support more effective communication.

[0080] The communication system can further include a learning management unit that manages the user's learning progress. The learning management unit has the function of managing the user's sign language and speech learning progress. For example, it can record a list of sign languages ​​that the user has learned and display the progress. It can also record a list of speech sounds that the user has learned and display the progress. Furthermore, the learning management unit can provide feedback according to the user's learning progress. For example, if the user has mastered a specific sign language or speech sound, it can display a message of praise. This makes it possible to manage the user's learning progress and improve their motivation.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The sign language recognition unit recognizes sign language using a camera device, such as a tablet's built-in camera or an externally connected camera, and uses image processing technology and machine learning models to analyze sign language movements and recognize the content of the sign language. Step 2: The speech conversion unit converts the sign language recognized by the sign language recognition unit into speech. For example, it converts the sign language into speech using speech synthesis technology or natural language processing technology, analyzes the content of the sign language, and generates appropriate speech. Step 3: The speech recognition unit recognizes the speech of the person being spoken to. For example, it uses a speech recognition engine or a machine learning model to recognize the speech of the person being spoken to, analyzes the content of the speech, and generates an appropriate sign language image. Step 4: The sign language image generator converts the speech recognized by the speech recognition unit into a sign language image. For example, it converts the speech into a sign language image using animation generation technology or a sign language database, analyzes the content of the speech, and generates an appropriate sign language image.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 sign language recognition unit that recognizes sign language using a camera device; a speech conversion unit that converts the sign language recognized by the sign language recognition unit into speech; a speech recognition unit that recognizes the speech of a conversation partner; a sign language image generation unit that converts the voice recognized by the voice recognition unit into a sign language image. A system characterized by:

2. The sign language recognition unit A dataset is used that includes not only the sign language actions but also facial expressions and body movements when performing the sign language.

2. The system of claim 1.

3. The sign language recognition unit Be able to recognize non-verbal communication, including gestures and body language other than the sign language mentioned above.

2. The system of claim 1.

4. The sign language recognition unit By taking into account not only the hand movement but also the hand position and hand speed, more accurate sign language recognition is achieved.

2. The system of claim 1.

5. The voice recognition unit Taking into account the intonation and rhythm of the voice, a more natural sign language video is generated.

2. The system of claim 1.

6. The sign language recognition unit An emotion estimation function is used to estimate the emotion when the sign language is used, and speech conversion is performed according to the emotion.

2. The system of claim 1.

7. The sign language recognition unit Using emotion estimation functionality, the system provides real-time feedback on the user's emotions while learning sign language data, improving the accuracy of learning.

2. The system of claim 1.

8. The voice recognition unit The emotion estimation function is used to estimate the emotion of the speaker when converting the speech into a sign language image, and a sign language image is generated according to that emotion.

2. The system of claim 1.

Images