JP2025058129A - system - Google Patents

Abstract

To enable a person who is in a state in which it is difficult to output voice to reproduce past voice by combining gaze tracking and voice reproduction.SOLUTION: A system comprises: means for tracking a line of sight of a person in a state in which voice output is difficult; and means for reproducing past voice based on information on the tracked line of sight.SELECTED DRAWING: Figure 1

Description

The technology disclosed herein relates to a system.

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 a description of the chatbot character and an associated instruction sentence, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

JP 2022-180282 A

The present invention aims to enable people who have difficulty outputting voice to reproduce past voices by combining gaze tracking and voice reproduction.

A system including a means for tracking the gaze of a person who is in a state where it is difficult to output speech, and a means for reproducing past speech based on the tracked gaze information.

1 is a conceptual diagram showing an example of a configuration of a data processing system according to a first embodiment. 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. FIG. 11 is a conceptual diagram showing an example of a configuration of a data processing system according to a second embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. FIG. 13 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. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped.

An example of an embodiment of a system according to the disclosed technology is described below with reference to the attached drawings.

First, let us explain the terminology used in the following explanation.

In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be one 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).

In the following embodiments, a signed random access memory (RAM) is a memory in which information is temporarily stored and is used by the processor as a working memory.

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

In the following embodiments, a communication I/F (Interface) with a code is an interface including a communication processor and 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).

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. In addition, in this specification, the same concept as "A and/or B" is also applied when three or more things are expressed by connecting them with "and/or."

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

As shown in FIG. 1, the 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.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

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.

The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or a finger) to receive user input by the touch of the indicator. 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.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form of expression that the user can perceive (e.g., voice and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The 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.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing 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 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.

Storage 32 stores a data generation model 58 and an emotion identification model 59. Data generation model 58 and emotion identification model 59 are used by the identification processing unit 290.

In the smart device 14, the reception output process is performed by the processor 46. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, 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). Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where it is difficult to output voice, and a neural network for reproducing past voice based on the tracked gaze information. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines it with past voice data to generate voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is having difficulty outputting voice, and a voice synthesis module for reproducing past voice based on the tracked gaze information. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is having difficulty outputting voice, and a voice processing unit for reproducing past voice based on the tracked gaze information. Specifically, the infrared sensor detects eye movements and transmits the information to the voice processing unit, which processes the voice in combination with past voice data.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The voice generated by the neural network is output, allowing people with difficulty in speech output to reproduce past voices.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The voice synthesis module outputs the synthesized voice, allowing people who have difficulty in voice output to reproduce past voices.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The voice processed by the voice processing unit is output, so that a person who has difficulty in voice output can reproduce the past voice.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where voice output is difficult, a neural network for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines the information with past voice data to generate voice. Furthermore, the emotion engine analyzes the gaze information and voice data, estimates emotions, and generates appropriate emotional expressions when playing back voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is in a state where voice output is difficult, a voice synthesis module for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice. Furthermore, the emotion engine adjusts the voice in real time based on the gaze information and emotion estimation results to reflect appropriate emotional expressions.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is in a state where voice output is difficult, a voice processing unit for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the infrared sensor measures the position and movement of the eyes with infrared rays, and transmits the information to the voice processing unit, which combines it with past voice data to process the voice. Furthermore, the emotion engine adjusts voice synthesis parameters based on the gaze information and emotion estimation results, and performs real-time voice processing according to changes in emotions.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.

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 for 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.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). 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 a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to 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 while using the data generation model 58.

In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart device 14.

[Second embodiment]

Figure 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 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.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

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.

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 the voice according to instructions from the processor 46.

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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the 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 performed in a secure state.

Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 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.

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 processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. Data generation model 58 and emotion identification model 59 are used by the identification processing unit 290.

In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, 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). Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where it is difficult to output voice, and a neural network for reproducing past voice based on the tracked gaze information. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines it with past voice data to generate voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is having difficulty outputting voice, and a voice synthesis module for reproducing past voice based on the tracked gaze information. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is having difficulty outputting voice, and a voice processing unit for reproducing past voice based on the tracked gaze information. Specifically, the infrared sensor detects eye movements and transmits the information to the voice processing unit, which processes the voice in combination with past voice data.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The voice generated by the neural network is output, allowing people with difficulty in speech output to reproduce past voices.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The voice synthesis module outputs the synthesized voice, allowing people who have difficulty in voice output to reproduce past voices.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The voice processed by the voice processing unit is output, so that a person who has difficulty in voice output can reproduce the past voice.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where voice output is difficult, a neural network for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines the information with past voice data to generate voice. Furthermore, the emotion engine analyzes the gaze information and voice data, estimates emotions, and generates appropriate emotional expressions when playing back voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is in a state where voice output is difficult, a voice synthesis module for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice. Furthermore, the emotion engine adjusts the voice in real time based on the gaze information and emotion estimation results to reflect appropriate emotional expressions.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is in a state where voice output is difficult, a voice processing unit for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the infrared sensor measures the position and movement of the eyes with infrared rays, and transmits the information to the voice processing unit, which combines it with past voice data to process the voice. Furthermore, the emotion engine adjusts voice synthesis parameters based on the gaze information and emotion estimation results, and performs real-time voice processing according to changes in emotions.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.

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 for 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.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). 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 a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to 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 while using the data generation model 58.

In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart glasses 214.

[Third embodiment]

Figure 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

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.

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 the voice according to instructions from the processor 46.

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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the 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 performed in a secure state.

Figure 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Figure 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.

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 processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. Data generation model 58 and emotion identification model 59 are used by the identification processing unit 290.

In the headset terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, 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). Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where it is difficult to output voice, and a neural network for reproducing past voice based on the tracked gaze information. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines it with past voice data to generate voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is having difficulty outputting voice, and a voice synthesis module for reproducing past voice based on the tracked gaze information. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is having difficulty outputting voice, and a voice processing unit for reproducing past voice based on the tracked gaze information. Specifically, the infrared sensor detects eye movements and transmits the information to the voice processing unit, which processes the voice in combination with past voice data.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The voice generated by the neural network is output, allowing people with difficulty in speech output to reproduce past voices.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The voice synthesis module outputs the synthesized voice, allowing people who have difficulty in voice output to reproduce past voices.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The voice processed by the voice processing unit is output, so that a person who has difficulty in voice output can reproduce the past voice.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where voice output is difficult, a neural network for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines the information with past voice data to generate voice. Furthermore, the emotion engine analyzes the gaze information and voice data, estimates emotions, and generates appropriate emotional expressions when playing back voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is in a state where voice output is difficult, a voice synthesis module for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice. Furthermore, the emotion engine adjusts the voice in real time based on the gaze information and emotion estimation results to reflect appropriate emotional expressions.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is in a state where voice output is difficult, a voice processing unit for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the infrared sensor measures the position and movement of the eyes with infrared rays, and transmits the information to the voice processing unit, which combines it with past voice data to process the voice. Furthermore, the emotion engine adjusts voice synthesis parameters based on the gaze information and emotion estimation results, and performs real-time voice processing according to changes in emotions.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.

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 for 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.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). 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 a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to 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 while using the data generation model 58.

In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.
[Fourth embodiment]

Figure 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 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.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

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.

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 the voice according to instructions from the processor 46.

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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the 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 performed in a secure state.

The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and legs. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 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.

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 processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. Data generation model 58 and emotion identification model 59 are used by the identification processing unit 290.

In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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 ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, 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). Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where it is difficult to output voice, and a neural network for reproducing past voice based on the tracked gaze information. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines it with past voice data to generate voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is having difficulty outputting voice, and a voice synthesis module for reproducing past voice based on the tracked gaze information. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is having difficulty outputting voice, and a voice processing unit for reproducing past voice based on the tracked gaze information. Specifically, the infrared sensor detects eye movements and transmits the information to the voice processing unit, which processes the voice in combination with past voice data.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The voice generated by the neural network is output, allowing people with difficulty in speech output to reproduce past voices.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The voice synthesis module outputs the synthesized voice, allowing people who have difficulty in voice output to reproduce past voices.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The voice processed by the voice processing unit is output, so that a person who has difficulty in voice output can reproduce the past voice.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

(Example 1)
The embodiment of the present invention is a system including a sensor for tracking the gaze of a person who is in a state where voice output is difficult, a neural network for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the sensor detects eye movements and inputs the information into the neural network, which combines the information with past voice data to generate voice. Furthermore, the emotion engine analyzes the gaze information and voice data, estimates emotions, and generates appropriate emotional expressions when playing back voice.
(Example 2)
The embodiment of the present invention is a system including a camera for tracking the gaze of a person who is in a state where voice output is difficult, a voice synthesis module for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the camera captures eye movements and transmits the information to the voice synthesis module, which combines the information with past voice data to synthesize voice. Furthermore, the emotion engine adjusts the voice in real time based on the gaze information and emotion estimation results to reflect appropriate emotional expressions.
(Example 3)
The embodiment of the present invention is a system including an infrared sensor for tracking the gaze of a person who is in a state where voice output is difficult, a voice processing unit for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions. Specifically, the infrared sensor measures the position and movement of the eyes with infrared rays, and transmits the information to the voice processing unit, which combines it with past voice data to process the voice. Furthermore, the emotion engine adjusts voice synthesis parameters based on the gaze information and emotion estimation results, and performs real-time voice processing according to changes in emotions.

The process flow for each example is explained below.

(Example 1)
Step 1: To detect the eye movements of a person who is having difficulty producing speech output, a sensor measures the position and movement of the eyes.
Step 2: The eye movement information detected by the sensor is input into the neural network and combined with past voice data to generate voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 2)
Step 1: To capture the eye movements of a person who is having difficulty outputting voice, a camera captures video of the eye position and movements.
Step 2: The eye movement video captured by the camera is sent to the voice synthesis module, where it is combined with past voice data to synthesize voice.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.
(Example 3)
Step 1: To detect the eye movements of a person who is having difficulty outputting voice, an infrared sensor measures the position and movement of the eyes using infrared rays.
Step 2: The eye movement information detected by the infrared sensor is sent to the voice processing unit, where it is combined with past voice data for voice processing.
Step 3: The emotion engine analyzes the gaze information and voice data, infers emotions, and generates appropriate emotional expressions. By combining the generated voice and emotional expressions, people who have difficulty outputting voice can reproduce past voices containing emotions.

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 the user input for 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.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). 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 a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to 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 while using the data generation model 58.

In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the robot 414.

The emotion identification model 59, which serves as an emotion engine, may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from mental states are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, 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 in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

These emotions are distributed in the three o'clock direction of emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on inside the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotions become.

Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery power, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions that belong to an area called "reaction" where sensation is dominant. The right half of the emotion map is lined with emotions that belong to an area called "situation" where situation recognition is dominant.

The emotion map defines two emotions that encourage learning. The first is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input to 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 learning 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 Figure 10. Figure 10 shows an example in which multiple emotions, "peace of mind," "calm," and "reassuring," have similar emotion values.

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.

In the above embodiment, an example has been described in which the specific processing program 56 is stored in the storage 32, 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 Universal Serial Bus (USB) 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.

The specific processing program 56 may also 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 in the computer 22 in response to a request from the data processing device 12.

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.

The various processors listed below can be used as hardware resources for executing specific processes. Examples of processors include a CPU, which is a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically to execute specific processes. All of these processors have built-in or connected memory, and all of these processors execute specific processes by using the memory.

The hardware resource that executes the specific process may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific process may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured by combining one or more CPUs with software, and this processor functions as a hardware resource that executes a specific process. Secondly, there is a configuration in which a processor is used that realizes the functions of the 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 various processors mentioned above as hardware resources.

More specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processing described above is merely an example. It goes without saying that unnecessary steps can be deleted, new steps can be added, and the processing order can be changed without departing from the spirit of the invention.

The above 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 explanation 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 description and illustrations, within the scope of the gist of the technology of the present disclosure. Also, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards described in this specification are incorporated by reference into this specification to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

The following is further disclosed regarding the above embodiment.

(Appendix 1)
A system including a means for tracking the gaze of a person who is in a state where it is difficult to output voice, and a means for reproducing past voice based on the tracked gaze information.
(Appendix 2)
In the system described in Appendix 1, the gaze tracking means is characterized by using a sensor that detects eye movement.
(Appendix 3)
In the system described in Supplementary Note 1, the voice reproduction means is characterized by using a neural network that learns past voice data and combines it with gaze information to generate voice.

(Appendix 4)
A system including a means for tracking the gaze of a person who is in a state where voice output is difficult, a means for reproducing past voice based on the tracked gaze information, and an emotion engine for recognizing the user's emotions.
(Appendix 5)
In the system described in Supplementary Note 4, the emotion engine is characterized by analyzing gaze information and voice data and estimating emotions in order to generate appropriate emotional expressions when reproducing voice.
(Appendix 6)
In the system described in Supplementary Note 4, the emotion engine is characterized in that it adjusts voice synthesis parameters based on gaze information and emotion estimation results in order to adjust the voice in real time in response to changes in emotion.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (3)

A system including a means for tracking the gaze of a person who is in a state where it is difficult to output speech, and a means for reproducing past speech based on the tracked gaze information. In the system described in claim 1, the gaze tracking means is characterized by using a sensor that detects eye movement. In the system described in claim 1, the voice reproduction means uses a neural network that learns past voice data and combines it with gaze information to generate voice.

Images