System

Abstract

An object of the system according to the embodiment is to maintain the quality of an animation work even when the voice of a voice actor is lost.SOLUTION: A system includes an analysis unit, a generation unit, and an application unit. The analysis unit analyzes characteristics and intonation of the voice of the voice actor. The generation unit generates a voice based on the characteristics and intonation of the voice of the voice actor analyzed by the analysis unit. The application unit applies the voice generated by the generation unit to the animation work.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, if a voice actor's voice is lost, the quality of the animation work may be reduced.

[0005] The system according to the embodiment aims to maintain the quality of an animated work even if the voice of a voice actor is lost. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and an application unit. The analysis unit analyzes the voice characteristics and intonation of a voice actor. The generation unit generates a voice based on the voice characteristics and intonation of the voice actor analyzed by the analysis unit. The application unit applies the voice generated by the generation unit to an animation work. [Effects of the Invention]

[0007] The system according to the embodiment can maintain the quality of the animation work even if the voice of the voice actor is lost. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The anime insurance system according to the embodiment of the present invention is a system that uses generative AI to reproduce the voice of a voice actor, in order to address the risk of anime works declining due to the loss of the voice of the voice actor. This allows the anime insurance system to maintain the popularity of an animation work even in situations where the voice actor is no longer able to perform the voice.

[0029] The animation insurance system according to the embodiment includes an analysis unit, a generation unit, and an application unit. The analysis unit analyzes the voice characteristics and intonation of a voice actor. For example, the analysis unit analyzes the voice actor's range, timbre, and pronunciation habits. The analysis unit can also analyze changes in pitch and volume of the voice actor. The analysis unit can also analyze the frequency components and formants of the voice actor. The generation unit generates a voice based on the voice characteristics and intonation of the voice actor analyzed by the analysis unit. For example, the generation unit generates a voice using voice synthesis technology. The generation unit can also generate a voice using a deep learning algorithm. The generation unit can also generate a voice using a generative model. The application unit applies the voice generated by the generation unit to an animation work. For example, the application unit synchronizes the generated voice with the mouth movements of an animated character. The application unit can also adjust the timing of the generated voice to match the animation scene. The application unit can also integrate the generated voice with the sound effects of the animation. This enables the animation insurance system to reproduce the voice of the voice actor and maintain the animation work.

[0030] The generation unit can use the voice actor's past performance data to generate a voice that is optimal for a specific scene or character. For example, the generation unit analyzes the voice actor's past performance data to generate a voice that is optimal for a specific scene or character. For example, it extracts emotional expression patterns from the past performance data and applies them to a new scene. The generation unit also uses the voice actor's past performance data to generate a voice that matches the character's personality and background. For example, it maintains consistency in the performance by changing the tone and pitch of the voice as the character grows. The generation unit also enhances emotional expression in a specific scene based on the voice actor's past performance data. For example, it references data from a past moving scene to reproduce a similar emotion in a new scene. In this way, by utilizing past performance data, new scenes can be generated while maintaining the consistency of the character.

[0031] The application unit can apply the generated voice to the entertainment field to generate character voices in games or movies. For example, the application unit applies the voice actor voice duplication technology to games to generate character voices in real time. For example, an interactive game in which a character's voice changes depending on the player's choices can be developed. The application unit also applies the voice actor voice duplication technology to movies to replace an actor's voice. For example, even if an actor is unable to participate in filming, the generation AI can be used to recreate their voice and continue film production. The application unit also applies the voice actor voice duplication technology to virtual reality (VR) to generate virtual character voices in real time. For example, when a character interacts with a user in a VR environment, the generation AI can be used to provide a natural voice. This allows the generated voice to be applied to other entertainment fields, enabling a wide range of applications.

[0032] The application unit can apply the generated voice to the education or medical fields to generate audio guides or narrations. For example, the application unit applies the voice actor's voice replication technology to the education field to generate audio guides for teaching materials. For example, the voice of a famous figure can be reproduced in a history class to provide students with a sense of realism. The application unit can also apply the voice actor's voice replication technology to the medical field to generate audio guides for patients. For example, pre-surgery explanations and rehabilitation guidance can be provided to patients using generation AI. The application unit can also apply the voice actor's voice replication technology to audio guides for museums and art galleries to provide commentary on exhibits. For example, the voice of a famous historical figure can be reproduced to explain the background of the exhibits. This allows the generated voices to be applied to a wide range of applications in the education and medical fields.

[0033] The analysis unit multidimensionally analyzes the characteristics of the voice actor's voice, enabling more precise voice generation. For example, the analysis unit multidimensionally analyzes the characteristics of the voice actor's voice, analyzing the pitch, tone, rhythm, etc. of the voice in detail. This enables more precise voice generation. The analysis unit also multidimensionally analyzes the characteristics of the voice actor's voice, analyzing the frequency spectrum and formants of the voice. This reproduces the texture and acoustic characteristics of the voice. The analysis unit also multidimensionally analyzes the characteristics of the voice actor's voice, analyzing the temporal changes and dynamics of the voice. This allows for more precise reproduction of the voice intonation and emotional expression. This multidimensional analysis enables more precise voice generation.

[0034] The analysis unit can analyze the voice characteristics of a voice actor on a time axis and generate voice that corresponds to changes in the voice. For example, the analysis unit analyzes the voice characteristics of a voice actor on a time axis and generates voice that corresponds to changes in the voice. For example, it analyzes changes in voice tone and pitch over time and generates natural voice. The analysis unit also analyzes the voice characteristics of a voice actor on a time axis and reproduces voice changes in a specific scene. For example, it adjusts the voice tone and rhythm according to changes in emotion. The analysis unit also analyzes the voice characteristics of a voice actor on a time axis and reproduces the voice dynamics and intonation. For example, it analyzes changes in voice strength and tempo and generates realistic voice. This makes it possible to generate natural voice through analysis on a time axis.

[0035] The analysis unit can apply the technology for analyzing the characteristics of a voice actor's voice to music production and podcast production. For example, the analysis unit applies the technology for analyzing the characteristics of a voice actor's voice to music production to reproduce the singer's voice. For example, if a singer is unable to perform live, their voice can be reproduced using a generation AI. The analysis unit also applies the technology for analyzing the characteristics of a voice actor's voice to podcast production to generate a narrator's voice. For example, if a narrator is unable to record, their voice can be reproduced using a generation AI. The analysis unit also applies the technology for analyzing the characteristics of a voice actor's voice to audiobook production to generate the reader's voice. For example, if a reader is unable to record, their voice can be reproduced using a generation AI. This allows for a wide range of applications when applied to music production and podcast production.

[0036] The analysis unit can apply the technology for analyzing the voice characteristics of a voice actor to linguistics research and the improvement of voice recognition technology. For example, the analysis unit applies the technology for analyzing the voice characteristics of a voice actor to linguistics research to analyze voice characteristics in detail. For example, the analysis unit compares the voice characteristics of different languages ​​to clarify the differences between the languages. The analysis unit also applies the technology for analyzing the voice characteristics of a voice actor to the improvement of voice recognition technology to increase recognition accuracy. For example, the analysis unit has a voice recognition system learn the voice characteristics to improve recognition accuracy. The analysis unit also applies the technology for analyzing the voice characteristics of a voice actor to the improvement of voice synthesis technology to generate more natural voices. For example, the analysis unit has a voice synthesis system learn the voice characteristics to generate natural voices. This allows for a wide range of applications when applied to linguistics research and the improvement of voice recognition technology.

[0037] The application unit can use the generation AI to reproduce not only the voice of the voice actor, but also the character's movements and facial expressions. For example, the application unit uses the generation AI to reproduce not only the voice of the voice actor, but also the character's movements and facial expressions. For example, it automatically generates the character's mouth movements to match the voice of the voice actor. The application unit also uses the generation AI to reproduce the character's facial expressions based on the voice of the voice actor. For example, it changes the character's facial expressions according to the tone and pitch of the voice. The application unit also uses the generation AI to reproduce the character's movements based on the voice of the voice actor. For example, it adjusts the character's movements according to the strength of the voice. This allows the character's movements and facial expressions to be reproduced, making it possible to create more realistic animation works.

[0038] The application unit can use the generation AI to optimize the texture and sound effects of the voice actor's voice to generate more realistic voice. For example, the application unit uses the generation AI to optimize the texture and sound effects of the voice actor's voice to generate more realistic voice. For example, it adjusts the echo and reverb of the voice. The application unit also uses the generation AI to optimize the texture of the voice actor's voice to enhance the sound effects in specific scenes. For example, it adjusts voice distortion and filters. The application unit also uses the generation AI to optimize the texture and sound effects of the voice actor's voice to reproduce the character's voice more realistically. For example, it adjusts voice compression and equalization. In this way, by optimizing the texture and sound effects of the voice, more realistic voice generation is possible.

[0039] The application unit can use the generative AI to apply the system not only to animation works, but also to the production of stage plays and radio dramas. For example, the application unit uses the generative AI to generate the voice of a character in a stage play. For example, if an actor is unable to appear, the generative AI is used to reproduce that voice. The application unit also uses the generative AI to generate the voice of a character in a radio drama. For example, if a voice actor is unable to record, the generative AI is used to reproduce that voice. The application unit also uses the generative AI to generate the voice of a reader in an audiobook. For example, if the reader is unable to record, the generative AI is used to reproduce that voice. This allows the use of generative AI to be applied to entertainment fields other than animation works.

[0040] The application unit can use the generative AI to automate and streamline the production process of animation works. For example, the application unit can use the generative AI to automatically generate voices for characters in animation works, thereby streamlining the production process. For example, this can shorten the recording time of voice actors. The application unit can also use the generative AI to automatically generate the movements and facial expressions of characters in animation works, thereby streamlining the production process. For example, this can reduce the workload of animators. The application unit can also use the generative AI to automatically generate sound effects for animation works, thereby streamlining the production process. For example, this can reduce the workload of sound engineers. This automation and streamlining of the production process can reduce production time and costs.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The anime insurance system can further include a voice recognition unit. The voice recognition unit recognizes the voice of a voice actor in real time and instantly analyzes the voice characteristics. For example, when a voice actor reads lines, the voice tone and pitch are instantly analyzed and feedback is sent to the generation unit. The voice recognition unit can also monitor changes in the voice of the voice actor in real time and provide data that allows the generation unit to generate more natural voices. Furthermore, the voice recognition unit can learn the voice characteristics of the voice actor and build a database to improve the accuracy of voice generation in the future. This enables more natural and precise voice generation through real-time voice recognition and analysis.

[0043] The generation unit may further include a voice filtering function. The voice filtering function performs noise removal and echo cancellation on the generated voice. For example, background noise contained in the generated voice may be removed to provide clearer voice. The voice filtering function may also cancel echoes in the generated voice to provide a more natural voice. Furthermore, the voice filtering function may enhance the texture of the voice by emphasizing specific frequency bands in the generated voice. This improves the quality of the generated voice and enables more realistic voice expression.

[0044] The generation unit can further include a voice effect function. The voice effect function applies various effects to the generated voice. For example, adding reverb to the generated voice adds depth to the voice. The voice effect function can also add distortion to the generated voice to enhance the voice expression in a specific scene. Furthermore, the voice effect function can also apply pitch shift to the generated voice to adjust the pitch of the character's voice. This allows for richer voice expression by applying various effects to the generated voice.

[0045] The generation unit can further include a voice modeling function. The voice modeling function models the characteristics of the voice actor's voice in detail to generate more precise voice. For example, the formants and resonance characteristics of the voice actor's voice can be modeled in detail and reflected in the generated voice. The voice modeling function can also model the temporal changes in the voice of the voice actor to generate natural voice. Furthermore, the voice modeling function can model the emotional nuances of the voice actor's voice to enhance emotional expression. This makes it possible to generate more realistic voice by modeling the characteristics of the voice actor's voice in detail.

[0046] The application unit may further include an audio synchronization function. The audio synchronization function synchronizes the generated audio with the movements of an animated character. For example, playing audio in accordance with the character's mouth movements can achieve a more natural expression. The audio synchronization function can also adjust the tone and pitch of the audio to match the character's facial expressions and gestures. Furthermore, the audio synchronization function can adjust the timing of the audio as the scene progresses to enhance the atmosphere of the scene. This allows for more realistic animation by synchronizing the generated audio with the character's movements.

[0047] The application unit may further include a voice transformation function. The voice transformation function converts the generated voice into various formats. For example, the generated voice may be translated into different languages ​​and applied to characters of different languages. The voice transformation function may also convert the generated voice into different voice qualities and apply to multiple characters. Furthermore, the voice transformation function may enhance the atmosphere of a scene by adapting the generated voice to different acoustic environments. This allows the generated voice to be converted into various formats, enabling a wide range of applications.

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

[0049] Step 1: The analysis unit analyzes the voice actor's vocal characteristics and intonation. For example, the analysis unit analyzes the voice actor's range, timbre, and pronunciation habits. It can also analyze changes in pitch and volume, frequency components, and formants. Step 2: The generator generates a voice based on the voice characteristics and intonation of the voice actor analyzed by the analyzer. For example, the generator generates the voice using voice synthesis technology, deep learning algorithms, or generative models. Step 3: The application unit applies the voice generated by the generation unit to the animation work. For example, the application unit can synchronize the generated voice with the mouth movements of an animated character, adjust the timing to match the scene, or integrate it with sound effects.

[0050] (Example 2) The anime insurance system according to the embodiment of the present invention is a system that uses generative AI to reproduce the voice of a voice actor, in order to address the risk of anime works declining due to the loss of the voice of the voice actor. This allows the anime insurance system to maintain the popularity of an animation work even in situations where the voice actor is no longer able to perform the voice.

[0051] The animation insurance system according to the embodiment includes an analysis unit, a generation unit, and an application unit. The analysis unit analyzes the voice characteristics and intonation of a voice actor. For example, the analysis unit analyzes the voice actor's range, timbre, and pronunciation habits. The analysis unit can also analyze changes in pitch and volume of the voice actor. The analysis unit can also analyze the frequency components and formants of the voice actor. The generation unit generates a voice based on the voice characteristics and intonation of the voice actor analyzed by the analysis unit. For example, the generation unit generates a voice using voice synthesis technology. The generation unit can also generate a voice using a deep learning algorithm. The generation unit can also generate a voice using a generative model. The application unit applies the voice generated by the generation unit to an animation work. For example, the application unit synchronizes the generated voice with the mouth movements of an animated character. The application unit can also adjust the timing of the generated voice to match the animation scene. The application unit can also integrate the generated voice with the sound effects of the animation. This enables the animation insurance system to reproduce the voice of the voice actor and maintain the animation work.

[0052] The generation unit can analyze the emotional nuances of the voice actor's voice in detail and generate voice corresponding to changes in emotion. For example, the generation unit analyzes the emotional nuances of the voice actor in detail and generates voice corresponding to changes in emotion. For example, the tone and pitch of the voice are adjusted to reflect emotions such as joy, sadness, and anger in the voice. The generation unit also analyzes the emotional nuances of the voice actor in detail and enhances emotional expression in specific scenes. For example, emotional changes are realistically reproduced by adding voice trembling to moving scenes. The generation unit also analyzes the emotional nuances of the voice actor in detail and generates voice corresponding to the character's emotions. For example, emotional changes are reflected in the voice by increasing the volume of the voice when the character is surprised. This allows for more realistic character expression by generating voice corresponding to changes in emotion.

[0053] The generation unit can use the voice actor's past performance data to generate a voice that is optimal for a specific scene or character. For example, the generation unit analyzes the voice actor's past performance data to generate a voice that is optimal for a specific scene or character. For example, it extracts emotional expression patterns from the past performance data and applies them to a new scene. The generation unit also uses the voice actor's past performance data to generate a voice that matches the character's personality and background. For example, it maintains consistency in the performance by changing the tone and pitch of the voice as the character grows. The generation unit also enhances emotional expression in a specific scene based on the voice actor's past performance data. For example, it references data from a past moving scene to reproduce a similar emotion in a new scene. In this way, by utilizing past performance data, new scenes can be generated while maintaining the consistency of the character.

[0054] The generation unit can use the emotion estimation function to estimate the emotional state of the voice actor in real time and generate voice based on that emotion. For example, the generation unit uses the emotion estimation function to estimate the emotional state of the voice actor in real time and generate voice based on that emotion. For example, if the voice actor is nervous, the tone of the voice is calmed. The generation unit also uses the emotion estimation function to analyze the emotional state of the voice actor in real time and enhance emotional expression according to the scene. For example, if the voice actor is happy, the volume of the voice is increased. The generation unit also uses the emotion estimation function to monitor the emotional state of the voice actor in real time and generate voice based on that data. For example, if the voice actor is sad, the tone of the voice is lowered. This allows for more natural character expression by generating voice according to emotions in real time.

[0055] The application unit can apply the generated voice to the entertainment field to generate character voices in games or movies. For example, the application unit applies the voice actor voice duplication technology to games to generate character voices in real time. For example, an interactive game in which a character's voice changes depending on the player's choices can be developed. The application unit also applies the voice actor voice duplication technology to movies to replace an actor's voice. For example, even if an actor is unable to participate in filming, the generation AI can be used to recreate their voice and continue film production. The application unit also applies the voice actor voice duplication technology to virtual reality (VR) to generate virtual character voices in real time. For example, when a character interacts with a user in a VR environment, the generation AI can be used to provide a natural voice. This allows the generated voice to be applied to other entertainment fields, enabling a wide range of applications.

[0056] The application unit can apply the generated voice to the education or medical fields to generate audio guides or narrations. For example, the application unit applies the voice actor's voice replication technology to the education field to generate audio guides for teaching materials. For example, the voice of a famous figure can be reproduced in a history class to provide students with a sense of realism. The application unit can also apply the voice actor's voice replication technology to the medical field to generate audio guides for patients. For example, pre-surgery explanations and rehabilitation guidance can be provided to patients using generation AI. The application unit can also apply the voice actor's voice replication technology to audio guides for museums and art galleries to provide commentary on exhibits. For example, the voice of a famous historical figure can be reproduced to explain the background of the exhibits. This allows the generated voices to be applied to a wide range of applications in the education and medical fields.

[0057] The application unit uses the emotion estimation function to generate audio guidance according to the user's emotion, thereby providing an interactive experience. The application unit, for example, uses the emotion estimation function to generate audio guidance according to the user's emotion. For example, if the user is excited, the tone of voice is raised to provide guidance. The application unit also uses the emotion estimation function to develop a system that provides audio guidance according to the user's emotion. For example, if the user is relaxed, the guidance is provided in a calm voice. The application unit also uses the emotion estimation function to generate audio guidance according to the user's emotion in real time. For example, if the user is surprised, the volume of the voice is raised to provide guidance. In this way, an interactive experience can be provided by generating audio guidance according to the user's emotion.

[0058] The analysis unit multidimensionally analyzes the characteristics of the voice actor's voice, enabling more precise voice generation. For example, the analysis unit multidimensionally analyzes the characteristics of the voice actor's voice, analyzing the pitch, tone, rhythm, etc. of the voice in detail. This enables more precise voice generation. The analysis unit also multidimensionally analyzes the characteristics of the voice actor's voice, analyzing the frequency spectrum and formants of the voice. This reproduces the texture and acoustic characteristics of the voice. The analysis unit also multidimensionally analyzes the characteristics of the voice actor's voice, analyzing the temporal changes and dynamics of the voice. This allows for more precise reproduction of the voice intonation and emotional expression. This multidimensional analysis enables more precise voice generation.

[0059] The analysis unit can analyze the voice characteristics of a voice actor on a time axis and generate voice that corresponds to changes in the voice. For example, the analysis unit analyzes the voice characteristics of a voice actor on a time axis and generates voice that corresponds to changes in the voice. For example, it analyzes changes in voice tone and pitch over time and generates natural voice. The analysis unit also analyzes the voice characteristics of a voice actor on a time axis and reproduces voice changes in a specific scene. For example, it adjusts the voice tone and rhythm according to changes in emotion. The analysis unit also analyzes the voice characteristics of a voice actor on a time axis and reproduces the voice dynamics and intonation. For example, it analyzes changes in voice strength and tempo and generates realistic voice. This makes it possible to generate natural voice through analysis on a time axis.

[0060] The analysis unit can use the emotion estimation function to capture changes in the voice actor's emotions and generate voices in response to those changes. For example, the analysis unit uses the emotion estimation function to capture changes in the voice actor's emotions in real time and generate voices in response to those changes. For example, if the voice actor is happy, the tone of the voice is brightened. The analysis unit also uses the emotion estimation function to analyze changes in the voice actor's emotions and enhance emotional expression in specific scenes. For example, if the voice actor is sad, the tone of the voice is lowered. The analysis unit also uses the emotion estimation function to monitor changes in the voice actor's emotions and generate voices based on that data. For example, if the voice actor is surprised, the volume of the voice is increased. This makes it possible to generate voices in response to changes in emotions.

[0061] The analysis unit can apply the technology for analyzing the characteristics of a voice actor's voice to music production and podcast production. For example, the analysis unit applies the technology for analyzing the characteristics of a voice actor's voice to music production to reproduce the singer's voice. For example, if a singer is unable to perform live, their voice can be reproduced using a generation AI. The analysis unit also applies the technology for analyzing the characteristics of a voice actor's voice to podcast production to generate a narrator's voice. For example, if a narrator is unable to record, their voice can be reproduced using a generation AI. The analysis unit also applies the technology for analyzing the characteristics of a voice actor's voice to audiobook production to generate the reader's voice. For example, if a reader is unable to record, their voice can be reproduced using a generation AI. This allows for a wide range of applications when applied to music production and podcast production.

[0062] The analysis unit can apply the technology for analyzing the voice characteristics of a voice actor to linguistics research and the improvement of voice recognition technology. For example, the analysis unit applies the technology for analyzing the voice characteristics of a voice actor to linguistics research to analyze voice characteristics in detail. For example, the analysis unit compares the voice characteristics of different languages ​​to clarify the differences between the languages. The analysis unit also applies the technology for analyzing the voice characteristics of a voice actor to the improvement of voice recognition technology to increase recognition accuracy. For example, the analysis unit has a voice recognition system learn the voice characteristics to improve recognition accuracy. The analysis unit also applies the technology for analyzing the voice characteristics of a voice actor to the improvement of voice synthesis technology to generate more natural voices. For example, the analysis unit has a voice synthesis system learn the voice characteristics to generate natural voices. This allows for a wide range of applications when applied to linguistics research and the improvement of voice recognition technology.

[0063] The analysis unit can use the emotion estimation function to develop a system that provides voice feedback according to the user's emotion. For example, the analysis unit uses the emotion estimation function to develop a system that provides voice feedback according to the user's emotion. For example, if the user is happy, an encouraging voice is provided. The analysis unit also uses the emotion estimation function to build a system that provides voice feedback according to the user's emotion in real time. For example, if the user is sad, a comforting voice is provided. The analysis unit also uses the emotion estimation function to develop a system that provides voice feedback according to the user's emotion, thereby providing an interactive experience. For example, if the user is surprised, a surprised voice is provided. In this way, an interactive experience can be provided by providing voice feedback according to the user's emotion.

[0064] The application unit can use the generation AI to reproduce not only the voice of the voice actor, but also the character's movements and facial expressions. For example, the application unit uses the generation AI to reproduce not only the voice of the voice actor, but also the character's movements and facial expressions. For example, it automatically generates the character's mouth movements to match the voice of the voice actor. The application unit also uses the generation AI to reproduce the character's facial expressions based on the voice of the voice actor. For example, it changes the character's facial expressions according to the tone and pitch of the voice. The application unit also uses the generation AI to reproduce the character's movements based on the voice of the voice actor. For example, it adjusts the character's movements according to the strength of the voice. This allows the character's movements and facial expressions to be reproduced, making it possible to create more realistic animation works.

[0065] The application unit can use the generation AI to optimize the texture and sound effects of the voice actor's voice to generate more realistic voice. For example, the application unit uses the generation AI to optimize the texture and sound effects of the voice actor's voice to generate more realistic voice. For example, it adjusts the echo and reverb of the voice. The application unit also uses the generation AI to optimize the texture of the voice actor's voice to enhance the sound effects in specific scenes. For example, it adjusts voice distortion and filters. The application unit also uses the generation AI to optimize the texture and sound effects of the voice actor's voice to reproduce the character's voice more realistically. For example, it adjusts voice compression and equalization. In this way, by optimizing the texture and sound effects of the voice, more realistic voice generation is possible.

[0066] The application unit can use the emotion estimation function to enhance the emotional expression of a character and create a work that appeals to the viewer's emotions. The application unit, for example, uses the emotion estimation function to enhance the emotional expression of a character and create a work that appeals to the viewer's emotions. For example, the application unit adjusts the tone and pitch of a character's voice according to the emotion. The application unit also uses the emotion estimation function to enhance the emotional expression of a character and realistically reproduce emotional changes in a specific scene. For example, when a character is surprised, the volume of the character's voice is increased. The application unit also uses the emotion estimation function to enhance the emotional expression of a character and create a scene that appeals to the viewer's emotions. For example, when a character is sad, the tone of the character's voice is lowered. In this way, the character's emotional expression can be enhanced to create a work that appeals to the viewer's emotions.

[0067] The application unit can use the generative AI to apply the system not only to animation works, but also to the production of stage plays and radio dramas. For example, the application unit uses the generative AI to generate the voice of a character in a stage play. For example, if an actor is unable to appear, the generative AI is used to reproduce that voice. The application unit also uses the generative AI to generate the voice of a character in a radio drama. For example, if a voice actor is unable to record, the generative AI is used to reproduce that voice. The application unit also uses the generative AI to generate the voice of a reader in an audiobook. For example, if the reader is unable to record, the generative AI is used to reproduce that voice. This allows the use of generative AI to be applied to entertainment fields other than animation works.

[0068] The application unit can use the generative AI to automate and streamline the production process of animation works. For example, the application unit can use the generative AI to automatically generate voices for characters in animation works, thereby streamlining the production process. For example, this can shorten the recording time of voice actors. The application unit can also use the generative AI to automatically generate the movements and facial expressions of characters in animation works, thereby streamlining the production process. For example, this can reduce the workload of animators. The application unit can also use the generative AI to automatically generate sound effects for animation works, thereby streamlining the production process. For example, this can reduce the workload of sound engineers. This automation and streamlining of the production process can reduce production time and costs.

[0069] The application unit can use the emotion estimation function to develop a system that analyzes the viewer's emotional reactions in real time and dynamically changes the content of a work. For example, the application unit uses the emotion estimation function to develop a system that analyzes the viewer's emotional reactions in real time and dynamically changes the content of a work. For example, if the viewer is surprised, the tension is increased in the next scene. The application unit also uses the emotion estimation function to build a system that analyzes the viewer's emotional reactions in real time and dynamically adjusts the content of a work. For example, if the viewer is sad, a moving development is added in the next scene. The application unit also uses the emotion estimation function to monitor the viewer's emotional reactions in real time and develop a system that dynamically changes the content of a work based on that data. For example, if the viewer is happy, humor is added in the next scene. This makes it possible to provide a more interactive viewing experience by dynamically changing the content of a work based on the viewer's emotional reactions.

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

[0071] The anime insurance system can further include a voice recognition unit. The voice recognition unit recognizes the voice of a voice actor in real time and instantly analyzes the voice characteristics. For example, when a voice actor reads lines, the voice tone and pitch are instantly analyzed and feedback is sent to the generation unit. The voice recognition unit can also monitor changes in the voice of the voice actor in real time and provide data that allows the generation unit to generate more natural voices. Furthermore, the voice recognition unit can learn the voice characteristics of the voice actor and build a database to improve the accuracy of voice generation in the future. This enables more natural and precise voice generation through real-time voice recognition and analysis.

[0072] The generation unit may further include a voice filtering function. The voice filtering function performs noise removal and echo cancellation on the generated voice. For example, background noise contained in the generated voice may be removed to provide clearer voice. The voice filtering function may also cancel echoes in the generated voice to provide a more natural voice. Furthermore, the voice filtering function may enhance the texture of the voice by emphasizing specific frequency bands in the generated voice. This improves the quality of the generated voice and enables more realistic voice expression.

[0073] The generation unit can further include a voice effect function. The voice effect function applies various effects to the generated voice. For example, adding reverb to the generated voice adds depth to the voice. The voice effect function can also add distortion to the generated voice to enhance the voice expression in a specific scene. Furthermore, the voice effect function can also apply pitch shift to the generated voice to adjust the pitch of the character's voice. This allows for richer voice expression by applying various effects to the generated voice.

[0074] The generation unit can further include a voice modeling function. The voice modeling function models the characteristics of the voice actor's voice in detail to generate more precise voice. For example, the formants and resonance characteristics of the voice actor's voice can be modeled in detail and reflected in the generated voice. The voice modeling function can also model the temporal changes in the voice of the voice actor to generate natural voice. Furthermore, the voice modeling function can model the emotional nuances of the voice actor's voice to enhance emotional expression. This makes it possible to generate more realistic voice by modeling the characteristics of the voice actor's voice in detail.

[0075] The application unit may further include an audio synchronization function. The audio synchronization function synchronizes the generated audio with the movements of an animated character. For example, playing audio in accordance with the character's mouth movements can achieve a more natural expression. The audio synchronization function can also adjust the tone and pitch of the audio to match the character's facial expressions and gestures. Furthermore, the audio synchronization function can adjust the timing of the audio as the scene progresses to enhance the atmosphere of the scene. This allows for more realistic animation by synchronizing the generated audio with the character's movements.

[0076] The application unit may further include a voice transformation function. The voice transformation function converts the generated voice into various formats. For example, the generated voice may be translated into different languages ​​and applied to characters of different languages. The voice transformation function may also convert the generated voice into different voice qualities and apply to multiple characters. Furthermore, the voice transformation function may enhance the atmosphere of a scene by adapting the generated voice to different acoustic environments. This allows the generated voice to be converted into various formats, enabling a wide range of applications.

[0077] The application unit may further include a voice interaction function. The voice interaction function generates voice through dialogue with the user. For example, when the user asks a question, the voice interaction function responds with generated voice. The voice interaction function can also estimate the user's emotional state and generate voice corresponding to that emotion. For example, if the user is excited, the voice interaction function can respond with a higher tone of voice. Furthermore, the voice interaction function can dynamically change the content of the voice based on the user's reaction. This enables more natural voice generation through interactive dialogue with the user.

[0078] The application unit may further include a voice feedback function. The voice feedback function generates voice based on the user's actions and reactions. For example, when the user performs a specific action, feedback is provided using the generated voice. The voice feedback function may also estimate the user's emotional state and provide feedback according to the emotion. For example, if the user is sad, a comforting voice may be provided. Furthermore, the voice feedback function may dynamically change the content of the feedback based on the user's reactions. This allows for a more interactive experience by providing voice feedback according to the user's actions and emotions.

[0079] The application unit can further include a voice guidance function. The voice guidance function estimates the user's emotional state and provides guidance according to that emotion. For example, if the user is relaxed, the guidance is provided in a calm voice. The voice guidance function can also analyze the user's emotional state in real time and provide guidance according to the situation. For example, if the user is excited, the guidance is provided in a higher voice tone. Furthermore, the voice guidance function can dynamically change the content of the guidance based on the user's reaction. This allows for a more interactive experience by providing voice guidance according to the user's emotions.

[0080] The application unit may further include a voice emotion function. The voice emotion function estimates the user's emotional state and generates voice corresponding to the emotion. For example, if the user is happy, it provides an encouraging voice. The voice emotion function may also analyze the user's emotional state in real time and enhance emotional expression in a specific scene. For example, if the user is sad, it may provide a comforting voice. Furthermore, the voice emotion function may dynamically change the content of the voice based on the user's reaction. This allows for a more interactive experience by generating voice corresponding to the user's emotion.

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

[0082] Step 1: The analysis unit analyzes the voice actor's vocal characteristics and intonation. For example, the analysis unit analyzes the voice actor's range, timbre, and pronunciation habits. It can also analyze changes in pitch and volume, frequency components, and formants. Step 2: The generator generates a voice based on the voice characteristics and intonation of the voice actor analyzed by the analyzer. For example, the generator generates the voice using voice synthesis technology, deep learning algorithms, or generative models. Step 3: The application unit applies the voice generated by the generation unit to the animation work. For example, the application unit can synchronize the generated voice with the mouth movements of an animated character, adjust the timing to match the scene, or integrate it with sound effects.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis department that analyzes the voice characteristics and intonation of voice actors, a generation unit that generates a voice based on the voice characteristics and intonation of the voice actor analyzed by the analysis unit; an application unit that applies the voice generated by the generation unit to an animation work; A system characterized by:

2. The generation unit Analyze the emotional nuances of the voice of the voice actor in detail and generate voices that respond to changes in emotion.

2. The system of claim 1.

3. The generation unit Using the voice actor's past performance data, the voice that is best suited to a specific scene or character is generated.

2. The system of claim 1.

4. The generation unit Estimate the emotional state of the voice actor in real time and generate a voice based on that emotion.

2. The system of claim 1.

5. The application unit includes: The generated voice is applied to the entertainment field to generate the voice of a character in a game or movie.

2. The system of claim 1.

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