System

Abstract

An object of the system according to the embodiment is to facilitate communication and information access by a hearing-impaired person in daily life.SOLUTION: A system includes a speech recognition unit, a text conversion unit, an speech synthesis unit, and a reporting unit. The voice recognition unit recognizes surrounding voice. The text conversion unit converts the voice recognized by the voice recognition unit into text. The speech synthesis unit converts the text converted by the text conversion unit into voice. The notifying unit notifies the hearing-impaired person of the voice converted by the speech synthesis unit.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 the problem that people with hearing impairments often experience difficulty in communicating and accessing information in their daily lives.

[0005] The system according to the embodiment aims to facilitate communication and information access in daily life for the hearing impaired. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a text conversion unit, a speech synthesis unit, and a notification unit. The speech recognition unit recognizes surrounding speech. The text conversion unit converts speech recognized by the speech recognition unit into text. The speech synthesis unit converts the text converted by the text conversion unit into speech. The notification unit notifies the hearing impaired person of the speech converted by the speech synthesis unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication and information access in daily life for the hearing impaired. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 service according to an embodiment of the present invention utilizes generative AI to solve communication and information access problems faced by elderly hearing-impaired people in their daily lives. This service supports speech communication and information gathering, helping elderly hearing-impaired people live more independently. As a result, the service solves communication and information access problems faced by elderly hearing-impaired people in their daily lives, enabling them to live more independently.

[0029] The service according to the embodiment includes a speech recognition unit, a text conversion unit, a speech synthesis unit, and a notification unit. The speech recognition unit recognizes surrounding speech. For example, the speech recognition unit recognizes conversations with family and friends. The speech recognition unit can also recognize speech from television and radio. The speech recognition unit can also recognize environmental sounds. For example, the speech recognition unit recognizes conversations with family and friends in real time and transmits the speech to the text conversion unit. The text conversion unit converts the speech recognized by the speech recognition unit into text. For example, the text conversion unit converts speech to text using a speech recognition algorithm. The text conversion unit can also perform pre-processing of the speech data to improve conversion accuracy. The text conversion unit converts speech data transmitted from the speech recognition unit into text in real time. For example, the text conversion unit converts speech to text using a speech recognition algorithm and transmits the text to the speech synthesis unit. The speech synthesis unit converts the text converted by the text conversion unit into speech. For example, the speech synthesis unit converts text to speech using a speech synthesis algorithm. The speech synthesis unit can also perform post-processing of the speech data to improve sound quality. Furthermore, the speech synthesis unit converts text data transmitted from the text conversion unit into speech in real time. For example, the speech synthesis unit converts text into speech using a speech synthesis algorithm and transmits the speech to the notification unit. The notification unit notifies the hearing-impaired person of the speech converted by the speech synthesis unit. For example, the notification unit provides visual notification. The notification unit can also provide tactile notification. The notification unit can also adjust the timing of notification. For example, the notification unit notifies the hearing-impaired person of the speech data transmitted from the speech synthesis unit in real time. As a result, the service according to the embodiment enables the hearing-impaired person to visually check surrounding speech information and communicate their intentions by voice.

[0030] The voice recognition unit can filter surrounding environmental sounds and remove noise. For example, the voice recognition unit can introduce technology for filtering surrounding environmental sounds and removing noise to improve the accuracy of voice recognition. For example, the voice recognition unit can analyze background sounds in real time and remove unnecessary sounds. The voice recognition unit can also introduce technology for emphasizing specific sound sources and suppressing other sounds. The voice recognition unit can also remove surrounding noise using, for example, noise canceling technology. This improves the accuracy of voice recognition.

[0031] The speech recognition unit can translate speech recognition results in real time and simultaneously convert speech in different languages ​​into text. The speech recognition unit can, for example, translate speech recognition results in real time and simultaneously convert speech in different languages ​​into text. For example, the speech recognition unit can translate English speech into Japanese and display it as text. The speech recognition unit can also translate Spanish speech into English and display it as text. The speech recognition unit can also translate French speech into German and display it as text. This allows speech in different languages ​​to be converted into text in real time.

[0032] The speech recognition unit is applied to sign language recognition and text conversion, thereby supporting communication with people who do not understand sign language. For example, the speech recognition unit applies speech recognition and text conversion to sign language recognition and text conversion, thereby supporting communication with people who do not understand sign language. For example, sign language can be converted into text in real time and displayed. The speech recognition unit can also convert sign language into speech in real time to communicate with people who do not understand sign language. The speech recognition unit can also translate sign language in real time and simultaneously convert sign language in different languages ​​into text. This supports communication with people who do not understand sign language.

[0033] The speech recognition unit can add a function to visually highlight the speech recognition results, thereby emphasizing important information. The speech recognition unit can add a function to visually highlight the speech recognition results, for example, to highlight important information. For example, important keywords can be displayed in bold or in color. The speech recognition unit can also display important phrases with an animation effect, for example. The speech recognition unit can also display important information in a pop-up window, for example. This allows important information to be visually highlighted.

[0034] The speech synthesis unit can add a function to customize a speech profile according to user preferences when synthesizing speech. For example, the speech synthesis unit can add a function to customize a speech profile according to user preferences when synthesizing speech. For example, the speech synthesis unit can adjust the pitch and speed of the speech. The speech synthesis unit can also customize the accent and intonation of the speech, for example. The speech synthesis unit can also change the gender and age of the speech, for example. This allows the speech profile to be customized according to user preferences.

[0035] The speech synthesis unit can automatically adjust the speed and pitch of text reading in accordance with the user's level of comprehension. The speech synthesis unit automatically adjusts, for example, the speed and pitch of text reading in accordance with the user's level of comprehension. For example, reading at a speed that is easy for the user to understand. The speech synthesis unit can also read at a pitch that is easy for the user to understand. The speech synthesis unit can also adjust the reading speed and pitch in real time in accordance with the user's level of comprehension. This makes it possible to automatically adjust the speed and pitch of text reading in accordance with the user's level of comprehension.

[0036] The speech synthesis unit can be applied to a navigation system for the visually impaired to provide voice guidance. The speech synthesis unit, for example, applies text-to-speech and speech synthesis to a navigation system for the visually impaired to provide voice guidance. For example, it provides route guidance and instructions on how to use public transportation by voice. The speech synthesis unit can also provide voice guidance in real time to enable the visually impaired to travel safely. The speech synthesis unit can also provide voice guidance continuously until the visually impaired reaches their destination, for example. This allows the system to be applied to a navigation system for the visually impaired to provide voice guidance.

[0037] The notification unit can analyze the user's past search history and behavioral patterns to improve the accuracy of information collection. The notification unit, for example, analyzes the user's past search history and behavioral patterns to improve the accuracy of information collection. For example, it collects related information based on keywords searched in the past. The notification unit can also analyze the user's behavioral patterns to provide optimal information. The notification unit can also improve the accuracy of information collection based on the user's interests and concerns, for example. This improves the accuracy of information collection.

[0038] The notification unit can notify the information collection results at the optimal timing based on the user's schedule. The notification unit, for example, notifies the information collection results at the optimal timing based on the user's schedule. For example, the notification is made during a time period when the user is not in a meeting. The notification unit can also make a notification, for example, before or after an important event for the user. The notification unit can also make a notification at the optimal timing based on the user's activity pattern, for example. This allows the information collection results to be notified at the optimal timing.

[0039] The notification unit can apply information collection and notification to a health management system to provide information about the user's health condition. The notification unit, for example, applies information collection and notification to a health management system to provide information about the user's health condition. For example, the notification unit can notify the user of health advice based on the user's health data. The notification unit can also, for example, monitor the user's health condition in real time and notify the user if an abnormality is detected. The notification unit can also provide information based on the user's health goals, for example. This makes it possible to provide information about the user's health condition.

[0040] The notification unit can convert the information collection results into a visual note or a mind map to make them easier to understand visually. The notification unit, for example, converts the information collection results into a visual note or a mind map to make them easier to understand visually. For example, important information can be displayed using diagrams or icons. The notification unit can also create a mind map to visually show the relevance of information. The notification unit can also create a visual note to make it easier to organize information. This makes it easier to understand the information collection results visually.

[0041] The notification unit can integrate and analyze data from multiple sensors to improve the accuracy of support in emergencies. The notification unit, for example, integrates and analyzes data from multiple sensors to improve the accuracy of support in emergencies. For example, data from a temperature sensor and a smoke sensor can be integrated to detect a fire. The notification unit can also integrate data from a heart rate sensor and a blood pressure sensor to monitor health conditions. The notification unit can also integrate data from a location information sensor and an acceleration sensor to detect an emergency, for example. This improves the accuracy of support in emergencies.

[0042] The notification unit can provide the optimal response to the support result in an emergency based on the user's location information. The notification unit, for example, provides the optimal response to the support result in an emergency based on the user's location information. For example, the notification unit can guide the user to the nearest evacuation site to the user's location. The notification unit can also guide the user to the optimal evacuation route based on the user's location information. The notification unit can also notify emergency contacts based on the user's location information. This makes it possible to provide the optimal response to the support result in an emergency.

[0043] The notification unit can apply the support in an emergency to an evacuation guidance system in the event of a disaster and provide audio guidance. The notification unit, for example, applies the support in an emergency to an evacuation guidance system in the event of a disaster and provides audio guidance. For example, it provides audio guidance on evacuation routes. The notification unit can also provide audio information on evacuation locations, for example. The notification unit can also provide audio guidance on emergency contact numbers in the event of a disaster, for example. This allows the system to be applied to an evacuation guidance system in the event of a disaster and provide audio guidance.

[0044] The notification unit can notify family and friends of the results of emergency support in real time, encouraging them to take prompt action. The notification unit, for example, can notify family and friends of the results of emergency support in real time, encouraging them to take prompt action. For example, the notification unit can automatically notify family when an emergency occurs. The notification unit can also, for example, notify friends when an emergency occurs. The notification unit can also, for example, notify emergency contacts when an emergency occurs. This allows family and friends to be notified of the results of emergency support in real time, encouraging them to take prompt action.

[0045] The notification unit can provide the results of daily life support at the optimal timing based on the user's schedule. The notification unit, for example, provides the results of daily life support at the optimal timing based on the user's schedule. For example, support is provided during times when the user is not busy. The notification unit can also provide support, for example, before and after an important event for the user. The notification unit can also provide support at the optimal timing based on the user's activity pattern, for example. This makes it possible to provide the results of daily life support at the optimal timing.

[0046] The notification unit can apply daily life support to an educational system to provide learning support. The notification unit, for example, applies daily life support to an educational system to provide learning support. For example, the learning content can be read aloud. The notification unit can also monitor the learning progress and provide appropriate feedback. The notification unit can also provide support based on the learning goals, for example. In this way, daily life support can be applied to an educational system to provide learning support.

[0047] The notification unit can convert the results of daily life support into a visual note or a mind map to make it easier to understand visually. The notification unit, for example, converts the results of daily life support into a visual note or a mind map to make it easier to understand visually. For example, important information is displayed using diagrams or icons. The notification unit can also create a mind map to visually show the relevance of information. The notification unit can also create a visual note to make it easier to organize information. This makes it easier to understand visually the results of daily life support.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The voice recognition unit can learn the characteristics of a user's voice and create an individual voice profile. For example, it can learn the user's tone and accent to improve the accuracy of voice recognition. The voice recognition unit can also monitor changes in the user's voice to detect changes in health status. The voice recognition unit can also perform individual voice filtering based on the user's voice characteristics, for example. This improves the accuracy of voice recognition and makes it possible to monitor the user's health status.

[0050] The voice recognition unit can execute specific actions when triggered by specific keywords. For example, if it recognizes the keyword "Turn on the lights," it can turn on the lights. Also, if it recognizes the keyword "Play music," it can play music. Also, if it recognizes the keyword "Lower the temperature," it can lower the temperature of the air conditioner. This makes it possible to operate home appliances using voice commands.

[0051] The voice recognition unit can learn the characteristics of a user's voice and create an individual voice profile. For example, it can learn the user's tone and accent to improve the accuracy of voice recognition. The voice recognition unit can also monitor changes in the user's voice to detect changes in health status. The voice recognition unit can also perform individual voice filtering based on the user's voice characteristics, for example. This improves the accuracy of voice recognition and makes it possible to monitor the user's health status.

[0052] The voice recognition unit can execute specific actions when triggered by specific keywords. For example, if it recognizes the keyword "Turn on the lights," it can turn on the lights. Also, if it recognizes the keyword "Play music," it can play music. Also, if it recognizes the keyword "Lower the temperature," it can lower the temperature of the air conditioner. This makes it possible to operate home appliances using voice commands.

[0053] The voice recognition unit can learn the characteristics of a user's voice and create an individual voice profile. For example, it can learn the user's tone and accent to improve the accuracy of voice recognition. The voice recognition unit can also monitor changes in the user's voice to detect changes in health status. The voice recognition unit can also perform individual voice filtering based on the user's voice characteristics, for example. This improves the accuracy of voice recognition and makes it possible to monitor the user's health status.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The speech recognition unit recognizes surrounding sounds. For example, it can recognize conversations with family and friends, sounds from the TV or radio, and environmental sounds. The speech recognition unit recognizes these sounds in real time and sends them to the text conversion unit. Step 2: The text conversion unit converts the speech recognized by the speech recognition unit into text. For example, a speech recognition algorithm can be used to convert speech to text, and preprocessing of the speech data can be performed to improve conversion accuracy. The text conversion unit converts the speech data sent from the speech recognition unit into text in real time and sends it to the speech synthesis unit. Step 3: The speech synthesis unit converts the text converted by the text conversion unit into speech. For example, a speech synthesis algorithm can be used to convert text into speech, and post-processing of the speech data can be performed to improve sound quality. The speech synthesis unit converts the text data sent from the text conversion unit into speech in real time and sends it to the notification unit. Step 4: The notification unit notifies the hearing-impaired person of the voice converted by the voice synthesis unit. For example, visual or tactile notification can be performed, and the timing of the notification can also be adjusted. The notification unit notifies the hearing-impaired person of the voice data sent from the voice synthesis unit in real time.

[0056] (Example 2) A service according to an embodiment of the present invention utilizes generative AI to solve communication and information access problems faced by elderly hearing-impaired people in their daily lives. This service supports speech communication and information gathering, helping elderly hearing-impaired people live more independently. As a result, the service solves communication and information access problems faced by elderly hearing-impaired people in their daily lives, enabling them to live more independently.

[0057] The service according to the embodiment includes a speech recognition unit, a text conversion unit, a speech synthesis unit, and a notification unit. The speech recognition unit recognizes surrounding speech. For example, the speech recognition unit recognizes conversations with family and friends. The speech recognition unit can also recognize speech from television and radio. The speech recognition unit can also recognize environmental sounds. For example, the speech recognition unit recognizes conversations with family and friends in real time and transmits the speech to the text conversion unit. The text conversion unit converts the speech recognized by the speech recognition unit into text. For example, the text conversion unit converts speech to text using a speech recognition algorithm. The text conversion unit can also perform pre-processing of the speech data to improve conversion accuracy. The text conversion unit converts speech data transmitted from the speech recognition unit into text in real time. For example, the text conversion unit converts speech to text using a speech recognition algorithm and transmits the text to the speech synthesis unit. The speech synthesis unit converts the text converted by the text conversion unit into speech. For example, the speech synthesis unit converts text to speech using a speech synthesis algorithm. The speech synthesis unit can also perform post-processing of the speech data to improve sound quality. Furthermore, the speech synthesis unit converts text data transmitted from the text conversion unit into speech in real time. For example, the speech synthesis unit converts text into speech using a speech synthesis algorithm and transmits the speech to the notification unit. The notification unit notifies the hearing-impaired person of the speech converted by the speech synthesis unit. For example, the notification unit provides visual notification. The notification unit can also provide tactile notification. The notification unit can also adjust the timing of notification. For example, the notification unit notifies the hearing-impaired person of the speech data transmitted from the speech synthesis unit in real time. As a result, the service according to the embodiment enables the hearing-impaired person to visually check surrounding speech information and communicate their intentions by voice.

[0058] The speech recognition unit estimates the emotion of the speaker and generates a text expression corresponding to the emotion. For example, during speech recognition, the speech recognition unit estimates the emotion of the speaker and generates a text expression corresponding to the emotion. For example, if the speaker is angry, the speech recognition unit uses an emphasis expression to reflect the emotion in the text. Furthermore, for example, if the speaker is happy, the speech recognition unit uses a positive expression to reflect the emotion in the text. Furthermore, for example, if the speaker is sad, the speech recognition unit uses a negative expression to reflect the emotion in the text. This makes it possible to generate a text expression that reflects the emotion of the speaker.

[0059] The voice recognition unit can filter surrounding environmental sounds and remove noise. For example, the voice recognition unit can introduce technology for filtering surrounding environmental sounds and removing noise to improve the accuracy of voice recognition. For example, the voice recognition unit can analyze background sounds in real time and remove unnecessary sounds. The voice recognition unit can also introduce technology for emphasizing specific sound sources and suppressing other sounds. The voice recognition unit can also remove surrounding noise using, for example, noise canceling technology. This improves the accuracy of voice recognition.

[0060] The speech recognition unit can translate speech recognition results in real time and simultaneously convert speech in different languages ​​into text. The speech recognition unit can, for example, translate speech recognition results in real time and simultaneously convert speech in different languages ​​into text. For example, the speech recognition unit can translate English speech into Japanese and display it as text. The speech recognition unit can also translate Spanish speech into English and display it as text. The speech recognition unit can also translate French speech into German and display it as text. This allows speech in different languages ​​to be converted into text in real time.

[0061] The speech recognition unit is applied to sign language recognition and text conversion, thereby supporting communication with people who do not understand sign language. For example, the speech recognition unit applies speech recognition and text conversion to sign language recognition and text conversion, thereby supporting communication with people who do not understand sign language. For example, sign language can be converted into text in real time and displayed. The speech recognition unit can also convert sign language into speech in real time to communicate with people who do not understand sign language. The speech recognition unit can also translate sign language in real time and simultaneously convert sign language in different languages ​​into text. This supports communication with people who do not understand sign language.

[0062] The speech recognition unit can add a function to visually highlight the speech recognition results, thereby emphasizing important information. The speech recognition unit can add a function to visually highlight the speech recognition results, for example, to highlight important information. For example, important keywords can be displayed in bold or in color. The speech recognition unit can also display important phrases with an animation effect, for example. The speech recognition unit can also display important information in a pop-up window, for example. This allows important information to be visually highlighted.

[0063] The speech recognition unit can use the emotion estimation function to provide emotional feedback based on the speech recognition results and monitor the user's emotional state in real time. The speech recognition unit can, for example, use the emotion estimation function to provide emotional feedback based on the speech recognition results and monitor the user's emotional state in real time. For example, if the user is feeling stressed, the speech recognition unit can make a suggestion to relax. The speech recognition unit can also provide positive feedback, for example, if the user is happy. The speech recognition unit can also provide an encouraging message, for example, if the user is sad. This makes it possible to monitor the user's emotional state in real time.

[0064] The speech synthesis unit can use the emotion estimation function when reading text aloud to generate a speech tone that corresponds to the emotion. For example, the speech synthesis unit can use the emotion estimation function when reading text aloud to generate a speech tone that corresponds to the emotion. For example, text that has a joyful emotion can be read aloud in a bright tone. The speech synthesis unit can also read text that has a sad emotion in a calm tone. For example, the speech synthesis unit can also read text that has an angry emotion in an emphasized tone. In this way, a speech tone that corresponds to the emotion can be generated.

[0065] The speech synthesis unit can add a function to customize a speech profile according to user preferences when synthesizing speech. For example, the speech synthesis unit can add a function to customize a speech profile according to user preferences when synthesizing speech. For example, the speech synthesis unit can adjust the pitch and speed of the speech. The speech synthesis unit can also customize the accent and intonation of the speech, for example. The speech synthesis unit can also change the gender and age of the speech, for example. This allows the speech profile to be customized according to user preferences.

[0066] The speech synthesis unit can automatically adjust the speed and pitch of text reading in accordance with the user's level of comprehension. The speech synthesis unit automatically adjusts, for example, the speed and pitch of text reading in accordance with the user's level of comprehension. For example, reading at a speed that is easy for the user to understand. The speech synthesis unit can also read at a pitch that is easy for the user to understand. The speech synthesis unit can also adjust the reading speed and pitch in real time in accordance with the user's level of comprehension. This makes it possible to automatically adjust the speed and pitch of text reading in accordance with the user's level of comprehension.

[0067] The speech synthesis unit can be applied to a navigation system for the visually impaired to provide voice guidance. The speech synthesis unit, for example, applies text-to-speech and speech synthesis to a navigation system for the visually impaired to provide voice guidance. For example, it provides route guidance and instructions on how to use public transportation by voice. The speech synthesis unit can also provide voice guidance in real time to enable the visually impaired to travel safely. The speech synthesis unit can also provide voice guidance continuously until the visually impaired reaches their destination, for example. This allows the system to be applied to a navigation system for the visually impaired to provide voice guidance.

[0068] The speech synthesis unit uses the emotion estimation function to provide emotional feedback based on the speech synthesis result, thereby monitoring the user's emotional state in real time. The speech synthesis unit, for example, uses the emotion estimation function to provide emotional feedback based on the speech synthesis result, thereby monitoring the user's emotional state in real time. For example, if the user is feeling stressed, the speech synthesis unit may suggest relaxing. The speech synthesis unit may also provide positive feedback, for example, if the user is happy. The speech synthesis unit may also provide an encouraging message, for example, if the user is sad. This allows the user's emotional state to be monitored in real time.

[0069] The notification unit can use the emotion estimation function when collecting information to preferentially collect information that interests the user. For example, the notification unit can use the emotion estimation function when collecting information to preferentially collect information that interests the user. For example, the notification unit can preferentially collect news that shows the user positive emotions. The notification unit can also preferentially collect information related to topics that interest the user. The notification unit can also preferentially collect information that interests the user based on, for example, the user's past search history or behavioral patterns. This allows the user to preferentially collect information that interests them.

[0070] The notification unit can analyze the user's past search history and behavioral patterns to improve the accuracy of information collection. The notification unit, for example, analyzes the user's past search history and behavioral patterns to improve the accuracy of information collection. For example, it collects related information based on keywords searched in the past. The notification unit can also analyze the user's behavioral patterns to provide optimal information. The notification unit can also improve the accuracy of information collection based on the user's interests and concerns, for example. This improves the accuracy of information collection.

[0071] The notification unit can notify the information collection results at the optimal timing based on the user's schedule. The notification unit, for example, notifies the information collection results at the optimal timing based on the user's schedule. For example, the notification is made during a time period when the user is not in a meeting. The notification unit can also make a notification, for example, before or after an important event for the user. The notification unit can also make a notification at the optimal timing based on the user's activity pattern, for example. This allows the information collection results to be notified at the optimal timing.

[0072] The notification unit can apply information collection and notification to a health management system to provide information about the user's health condition. The notification unit, for example, applies information collection and notification to a health management system to provide information about the user's health condition. For example, the notification unit can notify the user of health advice based on the user's health data. The notification unit can also, for example, monitor the user's health condition in real time and notify the user if an abnormality is detected. The notification unit can also provide information based on the user's health goals, for example. This makes it possible to provide information about the user's health condition.

[0073] The notification unit can convert the information collection results into a visual note or a mind map to make them easier to understand visually. The notification unit, for example, converts the information collection results into a visual note or a mind map to make them easier to understand visually. For example, important information can be displayed using diagrams or icons. The notification unit can also create a mind map to visually show the relevance of information. The notification unit can also create a visual note to make it easier to organize information. This makes it easier to understand the information collection results visually.

[0074] The notification unit can use the emotion estimation function to collect the user's emotional reactions to the information collection results and improve the accuracy of information provision based on the collected data. The notification unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the information collection results and improve the accuracy of information provision based on the collected data. For example, the notification unit can provide information with a higher number of positive reactions preferentially. The notification unit can also filter information with a higher number of negative reactions, for example. The notification unit can also monitor the user's emotional reactions in real time and improve the accuracy of information provision, for example. This improves the accuracy of information provision.

[0075] The notification unit can use the emotion estimation function to monitor the user's stress level and take appropriate action when providing support in an emergency. The notification unit can, for example, use the emotion estimation function to monitor the user's stress level and take appropriate action when providing support in an emergency. For example, if the user is feeling high stress, the notification unit can suggest relaxing. The notification unit can also provide a message to calm the user when the user is in a panic, for example. The notification unit can also monitor the user's stress level in real time and take appropriate action. This makes it possible to monitor the user's stress level and take appropriate action in an emergency.

[0076] The notification unit can integrate and analyze data from multiple sensors to improve the accuracy of support in emergencies. The notification unit, for example, integrates and analyzes data from multiple sensors to improve the accuracy of support in emergencies. For example, data from a temperature sensor and a smoke sensor can be integrated to detect a fire. The notification unit can also integrate data from a heart rate sensor and a blood pressure sensor to monitor health conditions. The notification unit can also integrate data from a location information sensor and an acceleration sensor to detect an emergency, for example. This improves the accuracy of support in emergencies.

[0077] The notification unit can provide the optimal response to the support result in an emergency based on the user's location information. The notification unit, for example, provides the optimal response to the support result in an emergency based on the user's location information. For example, the notification unit can guide the user to the nearest evacuation site to the user's location. The notification unit can also guide the user to the optimal evacuation route based on the user's location information. The notification unit can also notify emergency contacts based on the user's location information. This makes it possible to provide the optimal response to the support result in an emergency.

[0078] The notification unit can apply the support in an emergency to an evacuation guidance system in the event of a disaster and provide audio guidance. The notification unit, for example, applies the support in an emergency to an evacuation guidance system in the event of a disaster and provides audio guidance. For example, it provides audio guidance on evacuation routes. The notification unit can also provide audio information on evacuation locations, for example. The notification unit can also provide audio guidance on emergency contact numbers in the event of a disaster, for example. This allows the system to be applied to an evacuation guidance system in the event of a disaster and provide audio guidance.

[0079] The notification unit can notify family and friends of the results of emergency support in real time, encouraging them to take prompt action. The notification unit, for example, can notify family and friends of the results of emergency support in real time, encouraging them to take prompt action. For example, the notification unit can automatically notify family when an emergency occurs. The notification unit can also, for example, notify friends when an emergency occurs. The notification unit can also, for example, notify emergency contacts when an emergency occurs. This allows family and friends to be notified of the results of emergency support in real time, encouraging them to take prompt action.

[0080] The notification unit can use the emotion estimation function to collect the user's emotional reactions to the support results in an emergency and improve the accuracy of the support based on the collected emotional reactions. The notification unit can, for example, use the emotion estimation function to collect the user's emotional reactions to the support results in an emergency and improve the accuracy of the support based on the collected emotional reactions. For example, the notification unit can preferentially provide a support method that indicates a sense of relief to the user. The notification unit can also, for example, improve a support method that indicates anxiety to the user. The notification unit can also, for example, monitor the user's emotional reactions in real time and improve the accuracy of the support. This improves the accuracy of the support in an emergency.

[0081] The notification unit can use the emotion estimation function to provide support according to the user's emotional state when supporting daily life. The notification unit can, for example, use the emotion estimation function to provide support according to the user's emotional state when supporting daily life. For example, if the user is feeling stressed, the notification unit can make a suggestion to relax. The notification unit can also provide positive feedback when the user is happy, for example. The notification unit can also provide an encouraging message when the user is sad, for example. In this way, support according to the user's emotional state can be provided when supporting daily life.

[0082] The notification unit can provide the results of daily life support at the optimal timing based on the user's schedule. The notification unit, for example, provides the results of daily life support at the optimal timing based on the user's schedule. For example, support is provided during times when the user is not busy. The notification unit can also provide support, for example, before and after an important event for the user. The notification unit can also provide support at the optimal timing based on the user's activity pattern, for example. This makes it possible to provide the results of daily life support at the optimal timing.

[0083] The notification unit can apply daily life support to an educational system to provide learning support. The notification unit, for example, applies daily life support to an educational system to provide learning support. For example, the learning content can be read aloud. The notification unit can also monitor the learning progress and provide appropriate feedback. The notification unit can also provide support based on the learning goals, for example. In this way, daily life support can be applied to an educational system to provide learning support.

[0084] The notification unit can convert the results of daily life support into a visual note or a mind map to make it easier to understand visually. The notification unit, for example, converts the results of daily life support into a visual note or a mind map to make it easier to understand visually. For example, important information is displayed using diagrams or icons. The notification unit can also create a mind map to visually show the relevance of information. The notification unit can also create a visual note to make it easier to organize information. This makes it easier to understand visually the results of daily life support.

[0085] The notification unit can use the emotion estimation function to collect the user's emotional reactions to the results of daily life support and improve the accuracy of support based on the collected data. The notification unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the results of daily life support and improve the accuracy of support based on the collected data. For example, the notification unit can preferentially provide support methods that receive many positive reactions. The notification unit can also improve support methods that receive many negative reactions, for example. The notification unit can also monitor the user's emotional reactions in real time and improve the accuracy of support, for example. This improves the accuracy of daily life support.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The voice recognition unit can learn the characteristics of a user's voice and create an individual voice profile. For example, it can learn the user's tone and accent to improve the accuracy of voice recognition. The voice recognition unit can also monitor changes in the user's voice to detect changes in health status. The voice recognition unit can also perform individual voice filtering based on the user's voice characteristics, for example. This improves the accuracy of voice recognition and makes it possible to monitor the user's health status.

[0088] The voice recognition unit can estimate the user's emotions and recommend appropriate music based on the estimated emotions. For example, if the user is feeling stressed, the voice recognition unit can recommend relaxing music. The voice recognition unit can also recommend upbeat music if the user is happy. The voice recognition unit can also recommend soothing music if the user is sad. This makes it possible to recommend music that matches the user's emotions.

[0089] The voice recognition unit can execute specific actions when triggered by specific keywords. For example, if it recognizes the keyword "Turn on the lights," it can turn on the lights. Also, if it recognizes the keyword "Play music," it can play music. Also, if it recognizes the keyword "Lower the temperature," it can lower the temperature of the air conditioner. This makes it possible to operate home appliances using voice commands.

[0090] The voice recognition unit can estimate the user's emotions and provide appropriate feedback based on the estimated emotions. For example, if the user is feeling stressed, the voice recognition unit can suggest relaxing. The voice recognition unit can also provide positive feedback if the user is happy, for example. The voice recognition unit can also provide an encouraging message if the user is sad, for example. In this way, feedback can be provided according to the user's emotions.

[0091] The voice recognition unit can learn the characteristics of a user's voice and create an individual voice profile. For example, it can learn the user's tone and accent to improve the accuracy of voice recognition. The voice recognition unit can also monitor changes in the user's voice to detect changes in health status. The voice recognition unit can also perform individual voice filtering based on the user's voice characteristics, for example. This improves the accuracy of voice recognition and makes it possible to monitor the user's health status.

[0092] The voice recognition unit can estimate the user's emotions and recommend appropriate music based on the estimated emotions. For example, if the user is feeling stressed, the voice recognition unit can recommend relaxing music. The voice recognition unit can also recommend upbeat music if the user is happy. The voice recognition unit can also recommend soothing music if the user is sad. This makes it possible to recommend music that matches the user's emotions.

[0093] The voice recognition unit can execute specific actions when triggered by specific keywords. For example, if it recognizes the keyword "Turn on the lights," it can turn on the lights. Also, if it recognizes the keyword "Play music," it can play music. Also, if it recognizes the keyword "Lower the temperature," it can lower the temperature of the air conditioner. This makes it possible to operate home appliances using voice commands.

[0094] The voice recognition unit can estimate the user's emotions and provide appropriate feedback based on the estimated emotions. For example, if the user is feeling stressed, the voice recognition unit can suggest relaxing. The voice recognition unit can also provide positive feedback if the user is happy, for example. The voice recognition unit can also provide an encouraging message if the user is sad, for example. In this way, feedback can be provided according to the user's emotions.

[0095] The voice recognition unit can learn the characteristics of a user's voice and create an individual voice profile. For example, it can learn the user's tone and accent to improve the accuracy of voice recognition. The voice recognition unit can also monitor changes in the user's voice to detect changes in health status. The voice recognition unit can also perform individual voice filtering based on the user's voice characteristics, for example. This improves the accuracy of voice recognition and makes it possible to monitor the user's health status.

[0096] The voice recognition unit can estimate the user's emotions and recommend appropriate music based on the estimated emotions. For example, if the user is feeling stressed, the voice recognition unit can recommend relaxing music. The voice recognition unit can also recommend upbeat music if the user is happy. The voice recognition unit can also recommend soothing music if the user is sad. This makes it possible to recommend music that matches the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The speech recognition unit recognizes surrounding sounds. For example, it can recognize conversations with family and friends, sounds from the TV or radio, and environmental sounds. The speech recognition unit recognizes these sounds in real time and sends them to the text conversion unit. Step 2: The text conversion unit converts the speech recognized by the speech recognition unit into text. For example, a speech recognition algorithm can be used to convert speech to text, and preprocessing of the speech data can be performed to improve conversion accuracy. The text conversion unit converts the speech data sent from the speech recognition unit into text in real time and sends it to the speech synthesis unit. Step 3: The speech synthesis unit converts the text converted by the text conversion unit into speech. For example, a speech synthesis algorithm can be used to convert text into speech, and post-processing of the speech data can be performed to improve sound quality. The speech synthesis unit converts the text data sent from the text conversion unit into speech in real time and sends it to the notification unit. Step 4: The notification unit notifies the hearing-impaired person of the voice converted by the voice synthesis unit. For example, visual or tactile notification can be performed, and the timing of the notification can also be adjusted. The notification unit notifies the hearing-impaired person of the voice data sent from the voice synthesis unit in real time.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 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.

[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 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.

[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 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice recognition unit that recognizes surrounding voices; a text conversion unit that converts the speech recognized by the speech recognition unit into text; a speech synthesis unit that converts the text converted by the text conversion unit into speech; a notification unit that notifies a hearing-impaired person of the voice converted by the voice synthesis unit. A system characterized by:

2. The voice recognition unit Introducing technology to filter out ambient noise and eliminate noise 2. The system of claim 1.

3. The voice recognition unit Added visual highlighting of speech recognition results to highlight important information 2. The system of claim 1.

4. The speech synthesis unit Add a feature to customize voice profiles according to user preferences when synthesizing speech.

2. The system of claim 1.

5. The notification unit When collecting information, emotion estimation function is used to prioritize the collection of information that the user is interested in.

2. The system of claim 1.

6. The notification unit Using emotion estimation to monitor users' stress levels and provide appropriate support during emergencies 2. The system of claim 1.

7. The notification unit Using emotion estimation functions to provide support in daily life according to the user's emotional state 2. The system of claim 1.

8. The voice recognition unit Estimate the speaker's emotions and generate text expressions according to the emotions 2. The system of claim 1.

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