System

Abstract

An object of a system according to an embodiment is to realize low-cost and high-quality voice conversion in real time.SOLUTION: A system includes an input unit, an analysis unit, a conversion unit, and an output unit. The input unit inputs voice. The analysis unit analyzes the voice input by the input unit. The conversion unit converts the voice based on the parameter generated by the analysis unit. The output unit outputs the voice converted by the conversion unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to achieve low-cost, high-quality voice conversion in real time.

[0005] The system according to the embodiment aims to realize low-cost, high-quality voice conversion in real time. [Means for solving the problem]

[0006] A system according to an embodiment includes an input unit, an analysis unit, a conversion unit, and an output unit. The input unit inputs speech. The analysis unit analyzes the speech input by the input unit. The conversion unit converts the speech based on parameters generated by the analysis unit. The output unit outputs the speech converted by the conversion unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can realize low-cost, high-quality voice conversion in real time. [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) A voice conversion system according to an embodiment of the present invention provides low-cost, high-quality voice conversion. This system can be used for a wide range of applications, including real-time conversations between VTubers, crime prevention measures, and call center operations. The voice conversion system is designed to be easy to use and simple, even without specialized knowledge. For example, a user inputs voice into the voice conversion system. For example, the voice can be input through an input device such as a microphone. The voice conversion system then analyzes the input voice and generates parameters for voice conversion. This analysis uses AI to perform detailed analysis of the voice's characteristics, such as pitch, tone, and speed. The voice conversion system then performs voice conversion based on the generated parameters. This conversion uses AI to convert the input voice into high-quality converted voice. For example, a male voice can be converted into a female voice, or into the voice of a specific character. The converted voice is output in real time. For example, when a VTuber is having a real-time conversation, the converted voice can be streamed as is. Furthermore, as a crime prevention measure, the voice from a surveillance camera can be converted and output. Furthermore, in call center operations, the voice of an operator can be converted to respond to customers. This allows the voice conversion system to achieve high-quality voice conversion at low cost, making it suitable for a wide range of applications. This allows the voice conversion system to achieve high-quality voice conversion at low cost, making it suitable for a wide range of applications. It is also designed to be easy to use and can be easily used without specialized knowledge. For example, VTubers can easily convert their own voices and broadcast them. As a crime prevention measure, converting and outputting the audio from surveillance cameras makes it possible to implement more effective crime prevention measures. Furthermore, in call center operations, converting the voices of operators to respond to customers allows for more flexible responses.

[0029] The voice conversion system according to the embodiment includes an input unit, an analysis unit, a conversion unit, and an output unit. The input unit allows a user to input voice. To input voice, the user uses an input device such as a microphone. The input unit collects voice using, for example, a microphone. The input unit can also use a built-in microphone of a smartphone or a computer. Furthermore, the input unit can also be connected and used with an external microphone. The analysis unit analyzes the voice input by the input unit. The analysis unit analyzes features of the voice, such as pitch, tone, and speed. The analysis unit analyzes the features of the voice in detail using AI. For example, the analysis unit performs frequency analysis of the voice. The analysis unit can also perform spectral analysis. Furthermore, the analysis unit can also perform waveform analysis of the voice. The conversion unit converts the voice based on parameters generated by the analysis unit. For example, the conversion unit converts a male voice into a female voice. The conversion unit converts the voice with high quality using AI. For example, the conversion unit converts the pitch of the voice. The conversion unit can also convert the speed of the voice. Furthermore, the conversion unit can also perform tone conversion of the voice. The output unit outputs the voice converted by the conversion unit in real time. The output unit outputs the voice using, for example, a speaker. The output unit can also output the voice using headphones. The output unit can also output the voice using a Bluetooth (registered trademark) device. As a result, the voice conversion system according to the embodiment can consistently perform processes from voice input to analysis, conversion, and output.

[0030] The analysis unit can analyze the pitch, tone, and speed characteristics of the voice. The analysis unit, for example, analyzes the pitch of the voice. For example, the analysis unit measures the fundamental frequency of the voice. The analysis unit can also analyze the tone of the voice. For example, the analysis unit analyzes the timbre and quality of the voice. The analysis unit can also analyze the speed of the voice. For example, the analysis unit measures the speaking rate of the voice. In this way, the analysis unit can analyze detailed characteristics of the voice, enabling more accurate voice conversion. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the pitch, tone, and speed characteristics of the voice into AI, which can analyze these characteristics.

[0031] The conversion unit can convert a male voice into a specific female voice. The conversion unit, for example, converts a male voice into a female voice. For example, the conversion unit converts a male voice into a female voice by raising the pitch of the voice. The conversion unit can also adjust the tone of the voice. For example, the conversion unit converts a male voice into a female voice by changing the timbre of the voice. The conversion unit can also adjust the speed of the voice. For example, the conversion unit converts a male voice into a female voice by changing the speaking speed of the voice. This allows the conversion unit to convert into a voice of a specific gender, thereby expanding its uses. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input a male voice into AI, which then converts it into a female voice.

[0032] The conversion unit can convert into a specific voice of a specific character. The conversion unit, for example, converts into the voice of a specific character. For example, the conversion unit converts into the voice of an anime character. The conversion unit can also convert into the voice of a movie character. For example, the conversion unit learns the characteristics of the voice of a specific character and converts the voice based on those characteristics. In this way, the conversion unit can be applied to entertainment applications by converting into the voice of a specific character. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the characteristics of the voice of a specific character into AI, and the AI ​​can convert the voice based on those characteristics.

[0033] The output unit can output the converted audio in real time. The output unit outputs the converted audio in real time, for example. For example, the output unit can output the audio in real time using a speaker. The output unit can also output the audio in real time using headphones. Furthermore, the output unit can also output the audio in real time using a Bluetooth device. This allows the output unit to output the audio in real time, thereby meeting applications that require immediacy. Some or all of the above-mentioned processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the converted audio to AI, which then outputs the audio in real time.

[0034] The output unit can convert and output specific audio from a surveillance camera. The output unit, for example, converts and outputs audio from a surveillance camera. For example, the output unit analyzes the audio from a surveillance camera, converts the audio, and outputs it. The output unit can also filter the audio from a surveillance camera and emphasize and output specific audio. In this way, the output unit can be used for crime prevention measures by converting the audio from a surveillance camera. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the audio from a surveillance camera to AI, which converts the audio and outputs it.

[0035] The output unit can convert the voice of an operator to respond to customers. For example, in call center operations, the output unit converts the voice of an operator to respond to customers. For example, the output unit can analyze the voice of an operator and convert the voice to respond to customers. The output unit can also filter the voice of the operator and emphasize specific sounds to respond to customers. This allows the output unit to convert the voice of the operator, thereby enabling flexible responses in call center operations. Some or all of the above-mentioned processing in the output unit may be performed using, or without, AI, for example. For example, the output unit can input the voice of an operator to AI, which then converts the voice to respond to customers.

[0036] The input unit can analyze the user's past voice input history and select the optimal input method. The input unit, for example, analyzes the user's past voice input history. For example, the input unit preferentially suggests voice input methods that the user has frequently used in the past. The input unit can also select the most effective input method from the user's past voice input history. Furthermore, the input unit can also suggest the optimal input method for a specific situation based on the user's past voice input history. In this way, the input unit can provide the optimal input method to the user by analyzing the past voice input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past voice input history into AI, which can select the optimal input method.

[0037] The input unit can filter the user's current environmental sounds to remove noise when inputting voice. For example, when the user is in a noisy environment, the input unit automatically filters background noise to ensure clear voice input. For example, the input unit can use noise canceling technology to remove background noise. Furthermore, when the user is in a quiet environment, the input unit can also remove subtle noise to achieve high-quality voice input. For example, the input unit can detect and filter subtle noise using a high-sensitivity microphone. Furthermore, when the user is moving, the input unit can filter wind noise and traffic noise to optimize voice input. For example, the input unit can analyze environmental sounds while moving in real time and remove noise. This allows the input unit to filter environmental sounds to enable clear voice input. Some or all of the above-described processing in the input unit can be performed, for example, using AI, or can be performed without AI. For example, the input unit can input environmental sound data to AI, which can then filter the noise.

[0038] The input unit can select the optimal input means depending on the user's input method when performing voice input. For example, when the user selects voice input, the input unit optimizes microphone sensitivity to input voice clearly. For example, the input unit automatically adjusts microphone sensitivity. Furthermore, when the user selects text input, the input unit can optimize the keyboard layout to facilitate smooth input. For example, the input unit customizes the keyboard layout to suit the user's preferences. Furthermore, when the user selects gesture input, the input unit can improve gesture recognition accuracy to ensure accurate input. For example, the input unit recognizes gestures with high accuracy using a gesture recognition algorithm. This allows the input unit to select the optimal means depending on the user's input method, thereby improving usability. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's input method data into AI, which then selects the optimal input means.

[0039] When inputting voice, the input unit can prioritize inputting highly relevant voice by taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit prioritizes processing voice input related to that area. For example, the input unit acquires the user's geographical location information and prioritizes inputting relevant voice based on that information. Furthermore, when the user is traveling, the input unit can prioritize processing voice input related to tourist spots. For example, the input unit tracks the user's current location in real time and prioritizes inputting voice related to tourist spots. Furthermore, when the user is at home, the input unit can prioritize processing voice input from within the home. For example, the input unit prioritizes input of voice from within the home based on the location information of the user's home. In this way, the input unit can prioritize processing highly relevant voice by taking into account the geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI, which can then prioritize inputting highly relevant voice.

[0040] The input unit can analyze the user's social media activity and input related voices when receiving voice input. The input unit, for example, prioritizes voice input related to topics the user is talking about on social media and processes it accordingly. For example, the input unit analyzes the user's social media activity and prioritizes input of voices related to the activity. The input unit can also analyze the content of the user's social media posts and suggest related voice inputs. For example, the input unit analyzes the content of the user's posts and inputs voices related to the content. The input unit can also prioritize processing related voice inputs by referring to the activities of the user's friends on social media. For example, the input unit analyzes the content of the posts of the user's friends and inputs voices related to the content. In this way, the input unit can analyze the social media activity and prioritize processing related voices. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media data to AI, which can then input related voices.

[0041] The input unit can customize the input method by reflecting the user's past feedback when inputting voice. The input unit, for example, suggests an optimal voice input method based on feedback provided by the user in the past. For example, the input unit analyzes the user's past feedback and customizes the voice input method based on the feedback. The input unit can also improve the accuracy of the voice input by reflecting the user's past feedback. For example, the input unit optimizes a voice recognition algorithm based on the user's feedback. The input unit can also customize the voice input interface by referring to the user's past feedback. For example, the input unit changes the voice input settings according to the user's preferences. In this way, the input unit can provide the user with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's feedback data to AI, which can customize the input method.

[0042] During voice analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the voice. For example, the analysis unit performs a detailed analysis on important voice input. For example, the analysis unit analyzes the content of the voice in detail and extracts important information. The analysis unit can also perform a standard analysis on general voice input. For example, the analysis unit analyzes basic features of the voice and provides standard information. The analysis unit can also perform a simplified analysis on voice input with low importance. For example, the analysis unit analyzes simple features of the voice and provides simplified information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the voice, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input voice importance data to AI, which can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the audio when analyzing the audio. For example, the analysis unit applies a natural language processing algorithm to conversational audio. For example, the analysis unit analyzes the conversational audio and extracts information using natural language processing technology. The analysis unit can also apply a music analysis algorithm to musical audio. For example, the analysis unit analyzes musical audio and extracts information using music analysis technology. The analysis unit can also apply an environmental sound analysis algorithm to environmental audio. For example, the analysis unit analyzes environmental audio and extracts information using environmental sound analysis technology. In this way, the analysis unit applies an appropriate analysis algorithm depending on the category of the audio, thereby improving the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio category data into AI, which then applies an appropriate analysis algorithm.

[0044] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the past analysis results and adjusts the algorithm parameters. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit reduces analysis errors by using past analysis data. The analysis unit can also analyze the user's past analysis results and adjust the analysis parameters. For example, the analysis unit optimizes analysis settings based on the past analysis history. In this way, the analysis unit improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into AI, which can improve the accuracy of the analysis.

[0045] During voice analysis, the analysis unit can determine the analysis priority based on the time of voice submission. For example, the analysis unit prioritizes the most recent voice input for analysis. For example, the analysis unit determines the priority of voice based on the submission date and time. The analysis unit can also postpone analysis of older voice inputs. For example, the analysis unit adjusts the priority of voice based on the submission order. The analysis unit can also dynamically adjust the analysis priority based on the submission time. For example, the analysis unit tracks the submission time in real time and dynamically changes the priority. This enables the analysis unit to determine the analysis priority based on the submission time, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into AI, which then determines the analysis priority.

[0046] During audio analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio. For example, the analysis unit prioritizes analysis of highly relevant audio inputs. For example, the analysis unit determines the priority order based on the relevance of the audio content. The analysis unit can also postpone analysis of less relevant audio inputs. For example, the analysis unit adjusts the priority of audio based on the relevance of the topic. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the audio. For example, the analysis unit evaluates the relevance of audio in real time and dynamically changes the analysis order. This enables the analysis unit to adjust the analysis order based on the relevance of the audio, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio relevance data into AI, which can adjust the order of analysis.

[0047] During speech analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit displays the analysis results using technical terms or industry jargon. Furthermore, if the user has general knowledge, the analysis unit can also provide analysis results that use less technical terminology. For example, the analysis unit displays the analysis results using general language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit uses simple language for beginners, slightly more technical language for intermediate users, and more technical terminology for advanced users. This allows the analysis unit to provide more appropriate analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI, which then adjusts the way the analysis results are presented.

[0048] The conversion unit can adjust the level of conversion detail based on the features of the voice during voice conversion. The conversion unit adjusts the level of conversion detail based on, for example, the pitch or tone of the voice. For example, the conversion unit can analyze the pitch of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also analyze the tone of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also adjust the level of conversion detail based on the speed of the voice. For example, the conversion unit can analyze the speed of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also adjust the level of conversion detail based on the sound quality of the voice. For example, the conversion unit can analyze the sound quality of the voice in detail and adjust the level of conversion detail based on the result. This enables the conversion unit to adjust the level of conversion detail based on the features of the voice, thereby enabling more accurate voice conversion. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input voice feature data to AI, which can adjust the level of conversion detail.

[0049] The conversion unit can apply different conversion algorithms depending on the category of the audio during audio conversion. For example, the conversion unit applies a natural language processing algorithm to conversational audio. For example, the conversion unit analyzes the conversational audio and converts the audio using natural language processing technology. The conversion unit can also apply a music conversion algorithm to musical audio. For example, the conversion unit analyzes the musical audio and converts the audio using music conversion technology. The conversion unit can also apply an environmental sound conversion algorithm to environmental audio. For example, the conversion unit analyzes the environmental audio and converts the audio using environmental sound conversion technology. In this way, the conversion unit applies an appropriate conversion algorithm depending on the category of the audio, thereby improving conversion accuracy. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input audio category data to AI, which then applies an appropriate conversion algorithm.

[0050] During voice conversion, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, optimizes the conversion algorithm based on the user's past conversion results. For example, the conversion unit analyzes past conversion results and adjusts algorithm parameters. The conversion unit can also improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit reduces conversion errors by using past conversion data. The conversion unit can also analyze the user's past conversion results and adjust conversion parameters. For example, the conversion unit optimizes conversion settings based on past conversion history. In this way, the conversion unit improves the accuracy of the conversion by referring to the past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past conversion result data into AI, which can improve the accuracy of the conversion.

[0051] During voice conversion, the conversion unit can determine the priority of conversion based on the time of submission of the voice. For example, the conversion unit prioritizes conversion of the most recent voice input. For example, the conversion unit determines the priority of voice based on the submission date and time. The conversion unit can also postpone conversion of older submitted voice input. For example, the conversion unit adjusts the priority of voice based on the order of submission. The conversion unit can also dynamically adjust the priority of conversion based on the time of submission. For example, the conversion unit tracks the time of submission in real time and dynamically changes the priority. This enables efficient conversion by the conversion unit determining the priority of conversion based on the time of submission of the voice. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input submission time data into AI, which can determine the priority of conversion.

[0052] The conversion unit can adjust the order of conversion based on the relevance of the audio during audio conversion. For example, the conversion unit prioritizes conversion of highly relevant audio inputs. For example, the conversion unit determines the priority based on the relevance of the audio content. The conversion unit can also postpone conversion of less relevant audio inputs. For example, the conversion unit adjusts the priority of audio based on the relevance of the topic. The conversion unit can also dynamically adjust the order of conversion based on the relevance of the audio. For example, the conversion unit evaluates the relevance of audio in real time and dynamically changes the conversion order. This enables the conversion unit to adjust the order of conversion based on the relevance of the audio, thereby enabling efficient conversion. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input audio relevance data into AI, which can adjust the order of conversion.

[0053] During speech conversion, the conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise. For example, if the user has specialized knowledge, the conversion unit provides a conversion result that uses a lot of technical terminology. For example, the conversion unit displays the conversion result using technical terms or industry jargon. Furthermore, if the user has general knowledge, the conversion unit can also provide a conversion result that uses less technical terminology. For example, the conversion unit displays the conversion result using general language. Furthermore, the conversion unit can adjust the way the conversion result is presented according to the user's level of expertise. For example, the conversion unit uses simple language for beginners, slightly more technical language for intermediate users, and more technical terminology for advanced users. This allows the conversion unit to provide more appropriate conversion results by adjusting the use of technical terminology in the conversion according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input the user's level of expertise data into AI, which then adjusts the way the conversion result is presented.

[0054] The output unit can adjust the level of detail of the output based on the importance of the audio at the time of output. For example, the output unit provides detailed output for important audio input. For example, the output unit analyzes the content of the audio in detail and outputs important information with emphasis. The output unit can also provide standard output for general audio input. For example, the output unit analyzes basic features of the audio and provides standard information. The output unit can also provide simplified output for audio input with low importance. For example, the output unit analyzes simple features of the audio and provides simplified information. This allows the output unit to adjust the level of detail of the output based on the importance of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, or without, AI. For example, the output unit can input audio importance data to AI, which can adjust the level of detail of the output.

[0055] The output unit can apply different output algorithms depending on the category of the audio when outputting. The output unit applies a natural language processing algorithm to, for example, conversational audio. For example, the output unit analyzes the conversational audio and outputs the audio using natural language processing technology. The output unit can also apply a music output algorithm to musical audio. For example, the output unit analyzes musical audio and outputs the audio using music output technology. The output unit can also apply an environmental sound output algorithm to environmental audio. For example, the output unit analyzes environmental audio and outputs the audio using environmental sound output technology. In this way, the output unit applies an appropriate output algorithm depending on the category of the audio, thereby improving output accuracy. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input audio category data to AI, which then applies an appropriate output algorithm.

[0056] The output unit can improve the accuracy of the output by referring to the user's past output results at the time of output. The output unit, for example, optimizes the output algorithm based on the user's past output results. For example, the output unit analyzes past output results and adjusts algorithm parameters. The output unit can also improve the accuracy of the output by referring to the user's past output results. For example, the output unit reduces output errors by using past output data. The output unit can also analyze the user's past output results and adjust output parameters. For example, the output unit optimizes output settings based on past output history. In this way, the output unit improves the accuracy of the output by referring to the past output results. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input past output result data to AI, which can improve the accuracy of the output.

[0057] The output unit can determine the output priority based on the submission time of the audio at the time of output. The output unit, for example, prioritizes the most recent audio input for output. For example, the output unit determines the priority of the audio based on the submission date and time. The output unit can also output audio inputs that were submitted earlier later. For example, the output unit adjusts the priority of the audio based on the submission order. The output unit can also dynamically adjust the output priority based on the submission time. For example, the output unit tracks the submission time in real time and dynamically changes the priority. This enables the output unit to determine the output priority based on the submission time of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input submission time data to AI, and the AI ​​can determine the output priority.

[0058] The output unit can adjust the order of output based on the relevance of the audio at the time of output. For example, the output unit prioritizes and outputs highly relevant audio input. For example, the output unit determines the priority based on the relevance of the audio content. The output unit can also output less relevant audio input later. For example, the output unit adjusts the priority of audio based on the relevance of the topic. The output unit can also dynamically adjust the order of output based on the relevance of the audio. For example, the output unit evaluates the relevance of audio in real time and dynamically changes the output order. This enables the output unit to adjust the output order based on the relevance of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input audio relevance data to AI, and the AI ​​can adjust the order of output.

[0059] The output unit can adjust the use of technical terminology in the output according to the user's level of expertise. For example, if the user has specialized knowledge, the output unit provides an output result that uses a lot of technical terminology. For example, the output unit displays the output result using technical terms or industry jargon. Furthermore, if the user has general knowledge, the output unit can also provide an output result that uses less technical terminology. For example, the output unit displays the output result using general language. Furthermore, the output unit can adjust the way the output result is expressed according to the user's level of expertise. For example, the output unit uses simple language for beginners, slightly more technical language for intermediate users, and more technical terminology for advanced users. This allows the output unit to provide a more appropriate output result by adjusting the use of technical terminology in the output according to the user's level of expertise. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the user's level of expertise data into AI, which then adjusts the way the output result is expressed.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When the user inputs voice, the input unit can analyze the user's past voice input history and select the optimal input method. For example, the input unit can preferentially suggest a voice input method that the user has frequently used in the past. The input unit can also select the most effective input method from the user's past voice input history. Furthermore, the input unit can also suggest the optimal input method for a specific situation based on the user's past voice input history. In this way, the input unit can provide the optimal input method to the user by analyzing the past voice input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past voice input history to AI, which can select the optimal input method.

[0062] During audio analysis, the analysis unit can apply different analysis algorithms depending on the category of the audio. For example, the analysis unit applies a natural language processing algorithm to conversational audio. For example, the analysis unit analyzes the conversational audio and extracts information using natural language processing technology. The analysis unit can also apply a music analysis algorithm to musical audio. For example, the analysis unit analyzes musical audio and extracts information using music analysis technology. The analysis unit can also apply an environmental sound analysis algorithm to environmental audio. For example, the analysis unit analyzes environmental audio and extracts information using environmental sound analysis technology. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the audio, thereby improving analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio category data into AI, which then applies an appropriate analysis algorithm.

[0063] During voice conversion, the conversion unit can adjust the level of conversion detail based on voice features. For example, the conversion unit adjusts the level of conversion detail based on the pitch or tone of the voice. For example, the conversion unit can analyze the pitch of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also analyze the tone of the voice in detail and adjust the level of conversion detail based on the result. Furthermore, the conversion unit can adjust the level of conversion detail based on the speed of the voice. For example, the conversion unit can analyze the speed of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also adjust the level of conversion detail based on the sound quality of the voice. For example, the conversion unit can analyze the sound quality of the voice in detail and adjust the level of conversion detail based on the result. In this way, the conversion unit can adjust the level of conversion detail based on the voice features, thereby enabling more accurate voice conversion. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input voice feature data to AI, and the AI ​​can adjust the level of conversion detail.

[0064] The output unit can adjust the level of detail of the output based on the importance of the audio at the time of output. For example, the output unit provides detailed output for important audio input. For example, the output unit analyzes the content of the audio in detail and outputs important information with emphasis. The output unit can also provide standard output for general audio input. For example, the output unit analyzes basic features of the audio and provides standard information. The output unit can also provide simplified output for audio input with low importance. For example, the output unit analyzes simple features of the audio and provides simplified information. This allows the output unit to adjust the level of detail of the output based on the importance of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, or without, AI. For example, the output unit can input audio importance data to AI, which can adjust the level of detail of the output.

[0065] During voice analysis, the analysis unit can determine the analysis priority based on the time of voice submission. For example, the most recent voice input is given priority for analysis. For example, the analysis unit determines the priority of voice based on the submission date and time. The analysis unit can also postpone analysis of older voice inputs. For example, the analysis unit adjusts the priority of voice based on the order of submission. The analysis unit can also dynamically adjust the analysis priority based on the submission time. For example, the analysis unit tracks the submission time in real time and dynamically changes the priority. This enables the analysis unit to determine the analysis priority based on the submission time, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into AI, which then determines the analysis priority.

[0066] The conversion unit can apply different conversion algorithms depending on the category of the audio during audio conversion. For example, the conversion unit applies a natural language processing algorithm to conversational audio. For example, the conversion unit analyzes the conversational audio and converts the audio using natural language processing technology. The conversion unit can also apply a music conversion algorithm to musical audio. For example, the conversion unit analyzes the musical audio and converts the audio using music conversion technology. The conversion unit can also apply an environmental sound conversion algorithm to environmental audio. For example, the conversion unit analyzes the environmental audio and converts the audio using environmental sound conversion technology. In this way, the conversion unit applies an appropriate conversion algorithm depending on the category of the audio, thereby improving conversion accuracy. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input audio category data into AI, which then applies an appropriate conversion algorithm.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The user inputs voice into the input unit. The user inputs voice using an input device such as a microphone. The input unit can be the built-in microphone of a smartphone or computer, or an external microphone can be connected and used. Step 2: The analysis unit analyzes the voice input by the input unit. The analysis unit analyzes voice characteristics such as pitch, tone, and speed, and uses AI to perform frequency analysis, spectrum analysis, and waveform analysis of the voice. Step 3: The conversion unit converts the voice based on the parameters generated by the analysis unit. The conversion unit performs pitch conversion, speed conversion, and tone conversion of the voice, such as converting a male voice to a female voice. Step 4: The output unit outputs the audio converted by the conversion unit in real time using a speaker, headphones, or a Bluetooth device.

[0069] (Example 2) A voice conversion system according to an embodiment of the present invention provides low-cost, high-quality voice conversion. This system can be used for a wide range of applications, including real-time conversations between VTubers, crime prevention measures, and call center operations. The voice conversion system is designed to be easy to use and simple, even without specialized knowledge. For example, a user inputs voice into the voice conversion system. For example, the voice can be input through an input device such as a microphone. The voice conversion system then analyzes the input voice and generates parameters for voice conversion. This analysis uses AI to perform detailed analysis of the voice's characteristics, such as pitch, tone, and speed. The voice conversion system then performs voice conversion based on the generated parameters. This conversion uses AI to convert the input voice into high-quality converted voice. For example, a male voice can be converted into a female voice, or into the voice of a specific character. The converted voice is output in real time. For example, when a VTuber is having a real-time conversation, the converted voice can be streamed as is. Furthermore, as a crime prevention measure, the voice from a surveillance camera can be converted and output. Furthermore, in call center operations, the voice of an operator can be converted to respond to customers. This allows the voice conversion system to achieve high-quality voice conversion at low cost, making it suitable for a wide range of applications. This allows the voice conversion system to achieve high-quality voice conversion at low cost, making it suitable for a wide range of applications. It is also designed to be easy to use and can be easily used without specialized knowledge. For example, VTubers can easily convert their own voices and broadcast them. As a crime prevention measure, converting and outputting the audio from surveillance cameras makes it possible to implement more effective crime prevention measures. Furthermore, in call center operations, converting the voices of operators to respond to customers allows for more flexible responses.

[0070] The voice conversion system according to the embodiment includes an input unit, an analysis unit, a conversion unit, and an output unit. The input unit allows a user to input voice. To input voice, the user uses an input device such as a microphone. The input unit collects voice using, for example, a microphone. The input unit can also use a built-in microphone of a smartphone or a computer. Furthermore, the input unit can also be connected and used with an external microphone. The analysis unit analyzes the voice input by the input unit. The analysis unit analyzes features of the voice, such as pitch, tone, and speed. The analysis unit analyzes the features of the voice in detail using AI. For example, the analysis unit performs frequency analysis of the voice. The analysis unit can also perform spectral analysis. Furthermore, the analysis unit can also perform waveform analysis of the voice. The conversion unit converts the voice based on parameters generated by the analysis unit. For example, the conversion unit converts a male voice into a female voice. The conversion unit converts the voice with high quality using AI. For example, the conversion unit converts the pitch of the voice. The conversion unit can also convert the speed of the voice. Furthermore, the conversion unit can also perform tone conversion of the voice. The output unit outputs the voice converted by the conversion unit in real time. The output unit outputs the voice using, for example, a speaker. The output unit can also output the voice using headphones. The output unit can also output the voice using a Bluetooth device. As a result, the voice conversion system according to the embodiment can consistently perform processes from voice input to analysis, conversion, and output.

[0071] The analysis unit can analyze the pitch, tone, and speed characteristics of the voice. The analysis unit, for example, analyzes the pitch of the voice. For example, the analysis unit measures the fundamental frequency of the voice. The analysis unit can also analyze the tone of the voice. For example, the analysis unit analyzes the timbre and quality of the voice. The analysis unit can also analyze the speed of the voice. For example, the analysis unit measures the speaking rate of the voice. In this way, the analysis unit can analyze detailed characteristics of the voice, enabling more accurate voice conversion. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit can input the pitch, tone, and speed characteristics of the voice into AI, which can analyze these characteristics.

[0072] The conversion unit can convert a male voice into a specific female voice. The conversion unit, for example, converts a male voice into a female voice. For example, the conversion unit converts a male voice into a female voice by raising the pitch of the voice. The conversion unit can also adjust the tone of the voice. For example, the conversion unit converts a male voice into a female voice by changing the timbre of the voice. The conversion unit can also adjust the speed of the voice. For example, the conversion unit converts a male voice into a female voice by changing the speaking speed of the voice. This allows the conversion unit to convert into a voice of a specific gender, thereby expanding its uses. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input a male voice into AI, which then converts it into a female voice.

[0073] The conversion unit can convert into a specific voice of a specific character. The conversion unit, for example, converts into the voice of a specific character. For example, the conversion unit converts into the voice of an anime character. The conversion unit can also convert into the voice of a movie character. For example, the conversion unit learns the characteristics of the voice of a specific character and converts the voice based on those characteristics. In this way, the conversion unit can be applied to entertainment applications by converting into the voice of a specific character. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the characteristics of the voice of a specific character into AI, and the AI ​​can convert the voice based on those characteristics.

[0074] The output unit can output the converted audio in real time. The output unit outputs the converted audio in real time, for example. For example, the output unit can output the audio in real time using a speaker. The output unit can also output the audio in real time using headphones. Furthermore, the output unit can also output the audio in real time using a Bluetooth device. This allows the output unit to output the audio in real time, thereby meeting applications that require immediacy. Some or all of the above-mentioned processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the converted audio to AI, which then outputs the audio in real time.

[0075] The output unit can convert and output specific audio from a surveillance camera. The output unit, for example, converts and outputs audio from a surveillance camera. For example, the output unit analyzes the audio from a surveillance camera, converts the audio, and outputs it. The output unit can also filter the audio from a surveillance camera and emphasize and output specific audio. In this way, the output unit can be used for crime prevention measures by converting the audio from a surveillance camera. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the audio from a surveillance camera to AI, which converts the audio and outputs it.

[0076] The output unit can convert the voice of an operator to respond to customers. For example, in call center operations, the output unit converts the voice of an operator to respond to customers. For example, the output unit can analyze the voice of an operator and convert the voice to respond to customers. The output unit can also filter the voice of the operator and emphasize specific sounds to respond to customers. This allows the output unit to convert the voice of the operator, thereby enabling flexible responses in call center operations. Some or all of the above-mentioned processing in the output unit may be performed using, or without, AI, for example. For example, the output unit can input the voice of an operator to AI, which then converts the voice to respond to customers.

[0077] The input unit can specifically estimate the user's emotion and specifically adjust the timing of the voice input based on the estimated user's emotion. The input unit, for example, estimates the user's emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Next, the input unit adjusts the timing of the voice input based on the estimated user's emotion. For example, if the user is nervous, the timing of the voice input can be delayed to give the user time to relax. Also, if the user is relaxed, the timing of the voice input can be advanced to promote smooth conversation. Furthermore, if the user is in a hurry, the timing of the voice input can be optimized to enable a quick response. As a result, the input unit can adjust the timing of the voice input according to the user's emotion, enabling more natural conversation. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input the user's facial expression data to the generation AI, which may then estimate the emotion and adjust the timing of the voice input based on the emotion.

[0078] The input unit can analyze the user's past voice input history and select the optimal input method. The input unit, for example, analyzes the user's past voice input history. For example, the input unit preferentially suggests voice input methods that the user has frequently used in the past. The input unit can also select the most effective input method from the user's past voice input history. Furthermore, the input unit can also suggest the optimal input method for a specific situation based on the user's past voice input history. In this way, the input unit can provide the optimal input method to the user by analyzing the past voice input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past voice input history into AI, which can select the optimal input method.

[0079] The input unit can filter the user's current environmental sounds to remove noise when inputting voice. For example, when the user is in a noisy environment, the input unit automatically filters background noise to ensure clear voice input. For example, the input unit can use noise canceling technology to remove background noise. Furthermore, when the user is in a quiet environment, the input unit can also remove subtle noise to achieve high-quality voice input. For example, the input unit can detect and filter subtle noise using a high-sensitivity microphone. Furthermore, when the user is moving, the input unit can filter wind noise and traffic noise to optimize voice input. For example, the input unit can analyze environmental sounds while moving in real time and remove noise. This allows the input unit to filter environmental sounds to enable clear voice input. Some or all of the above-described processing in the input unit can be performed, for example, using AI, or can be performed without AI. For example, the input unit can input environmental sound data to AI, which can then filter the noise.

[0080] The input unit can select the optimal input means depending on the user's input method when performing voice input. For example, when the user selects voice input, the input unit optimizes microphone sensitivity to input voice clearly. For example, the input unit automatically adjusts microphone sensitivity. Furthermore, when the user selects text input, the input unit can optimize the keyboard layout to facilitate smooth input. For example, the input unit customizes the keyboard layout to suit the user's preferences. Furthermore, when the user selects gesture input, the input unit can improve gesture recognition accuracy to ensure accurate input. For example, the input unit recognizes gestures with high accuracy using a gesture recognition algorithm. This allows the input unit to select the optimal means depending on the user's input method, thereby improving usability. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's input method data into AI, which then selects the optimal input means.

[0081] The input unit can specifically estimate the user's emotions and specifically determine the priority of input voices based on the estimated user emotions. The input unit, for example, estimates the user's emotions. For example, the input unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The input unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. Next, the input unit determines the priority of input voices based on the estimated user emotions. For example, if the user is nervous, the input unit can prioritize important voice inputs. Alternatively, if the user is relaxed, the input unit can process all voice inputs equally. Furthermore, if the user is in a hurry, the input unit can prioritize urgent voice inputs. In this way, the input unit can prioritize important voices by determining the priority of voices based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input a user's facial expression data to the generation AI, which may infer emotions and prioritize voices based on the emotions.

[0082] When inputting voice, the input unit can prioritize inputting highly relevant voice by taking into account the user's geographical location information. For example, when the user is in a specific area, the input unit prioritizes processing voice input related to that area. For example, the input unit acquires the user's geographical location information and prioritizes inputting relevant voice based on that information. Furthermore, when the user is traveling, the input unit can prioritize processing voice input related to tourist spots. For example, the input unit tracks the user's current location in real time and prioritizes inputting voice related to tourist spots. Furthermore, when the user is at home, the input unit can prioritize processing voice input from within the home. For example, the input unit prioritizes input of voice from within the home based on the location information of the user's home. In this way, the input unit can prioritize processing highly relevant voice by taking into account the geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI, which can then prioritize inputting highly relevant voice.

[0083] The input unit can analyze the user's social media activity and input related voices when receiving voice input. The input unit, for example, prioritizes voice input related to topics the user is talking about on social media and processes it accordingly. For example, the input unit analyzes the user's social media activity and prioritizes input of voices related to the activity. The input unit can also analyze the content of the user's social media posts and suggest related voice inputs. For example, the input unit analyzes the content of the user's posts and inputs voices related to the content. The input unit can also prioritize processing related voice inputs by referring to the activities of the user's friends on social media. For example, the input unit analyzes the content of the posts of the user's friends and inputs voices related to the content. In this way, the input unit can analyze the social media activity and prioritize processing related voices. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media data to AI, which can then input related voices.

[0084] The input unit can customize the input method by reflecting the user's past feedback when inputting voice. The input unit, for example, suggests an optimal voice input method based on feedback provided by the user in the past. For example, the input unit analyzes the user's past feedback and customizes the voice input method based on the feedback. The input unit can also improve the accuracy of the voice input by reflecting the user's past feedback. For example, the input unit optimizes a voice recognition algorithm based on the user's feedback. The input unit can also customize the voice input interface by referring to the user's past feedback. For example, the input unit changes the voice input settings according to the user's preferences. In this way, the input unit can provide the user with an optimal input method by reflecting the past feedback. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's feedback data to AI, which can customize the input method.

[0085] The analysis unit can specifically estimate the user's emotions and specifically adjust the presentation method of the voice analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. Next, the analysis unit adjusts the presentation method of the voice analysis based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. On the other hand, if the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide an analysis result that focuses on the main points. As a result, the analysis unit can provide more appropriate analysis results by adjusting the presentation method of the analysis based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may infer the emotion and adjust the expression method of the analysis based on the emotion.

[0086] During voice analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the voice. For example, the analysis unit performs a detailed analysis on important voice input. For example, the analysis unit analyzes the content of the voice in detail and extracts important information. The analysis unit can also perform a standard analysis on general voice input. For example, the analysis unit analyzes basic features of the voice and provides standard information. The analysis unit can also perform a simplified analysis on voice input with low importance. For example, the analysis unit analyzes simple features of the voice and provides simplified information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the voice, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input voice importance data to AI, which can adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the category of the audio when analyzing the audio. For example, the analysis unit applies a natural language processing algorithm to conversational audio. For example, the analysis unit analyzes the conversational audio and extracts information using natural language processing technology. The analysis unit can also apply a music analysis algorithm to musical audio. For example, the analysis unit analyzes musical audio and extracts information using music analysis technology. The analysis unit can also apply an environmental sound analysis algorithm to environmental audio. For example, the analysis unit analyzes environmental audio and extracts information using environmental sound analysis technology. In this way, the analysis unit applies an appropriate analysis algorithm depending on the category of the audio, thereby improving the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio category data into AI, which then applies an appropriate analysis algorithm.

[0088] During voice analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the past analysis results and adjusts the algorithm parameters. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit reduces analysis errors by using past analysis data. The analysis unit can also analyze the user's past analysis results and adjust the analysis parameters. For example, the analysis unit optimizes analysis settings based on the past analysis history. In this way, the analysis unit improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into AI, which can improve the accuracy of the analysis.

[0089] The analysis unit can specifically estimate the user's emotions and specifically adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. Next, the analysis unit adjusts the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a short and concise analysis result. On the other hand, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a quick analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI, which may infer the emotion and adjust the length of the analysis based on the emotion.

[0090] During voice analysis, the analysis unit can determine the analysis priority based on the time of voice submission. For example, the analysis unit prioritizes the most recent voice input for analysis. For example, the analysis unit determines the priority of voice based on the submission date and time. The analysis unit can also postpone analysis of older voice inputs. For example, the analysis unit adjusts the priority of voice based on the submission order. The analysis unit can also dynamically adjust the analysis priority based on the submission time. For example, the analysis unit tracks the submission time in real time and dynamically changes the priority. This enables the analysis unit to determine the analysis priority based on the submission time, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into AI, which then determines the analysis priority.

[0091] During audio analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio. For example, the analysis unit prioritizes analysis of highly relevant audio inputs. For example, the analysis unit determines the priority order based on the relevance of the audio content. The analysis unit can also postpone analysis of less relevant audio inputs. For example, the analysis unit adjusts the priority of audio based on the relevance of the topic. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the audio. For example, the analysis unit evaluates the relevance of audio in real time and dynamically changes the analysis order. This enables the analysis unit to adjust the analysis order based on the relevance of the audio, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio relevance data into AI, which can adjust the order of analysis.

[0092] During speech analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit displays the analysis results using technical terms or industry jargon. Furthermore, if the user has general knowledge, the analysis unit can also provide analysis results that use less technical terminology. For example, the analysis unit displays the analysis results using general language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit uses simple language for beginners, slightly more technical language for intermediate users, and more technical terminology for advanced users. This allows the analysis unit to provide more appropriate analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into AI, which then adjusts the way the analysis results are presented.

[0093] The conversion unit can specifically estimate the user's emotion and specifically adjust the voice conversion method based on the estimated user's emotion. The conversion unit, for example, estimates the user's emotion. For example, the conversion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The conversion unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the conversion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Next, the conversion unit adjusts the voice conversion method based on the estimated user's emotion. For example, if the user is nervous, the voice can be converted to a calm tone. Also, if the user is relaxed, the voice can be converted to a natural tone. Furthermore, if the user is in a hurry, the conversion can be performed quickly and output immediately. This allows the conversion unit to adjust the voice conversion method based on the user's emotion, enabling more appropriate voice conversion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using AI, or may be performed without using AI. For example, the conversion unit may input the user's facial expression data into the generation AI, which may infer the user's emotion and adjust the voice conversion method based on the emotion.

[0094] The conversion unit can adjust the level of conversion detail based on the features of the voice during voice conversion. The conversion unit adjusts the level of conversion detail based on, for example, the pitch or tone of the voice. For example, the conversion unit can analyze the pitch of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also analyze the tone of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also adjust the level of conversion detail based on the speed of the voice. For example, the conversion unit can analyze the speed of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also adjust the level of conversion detail based on the sound quality of the voice. For example, the conversion unit can analyze the sound quality of the voice in detail and adjust the level of conversion detail based on the result. This enables the conversion unit to adjust the level of conversion detail based on the features of the voice, thereby enabling more accurate voice conversion. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input voice feature data to AI, which can adjust the level of conversion detail.

[0095] The conversion unit can apply different conversion algorithms depending on the category of the audio during audio conversion. For example, the conversion unit applies a natural language processing algorithm to conversational audio. For example, the conversion unit analyzes the conversational audio and converts the audio using natural language processing technology. The conversion unit can also apply a music conversion algorithm to musical audio. For example, the conversion unit analyzes the musical audio and converts the audio using music conversion technology. The conversion unit can also apply an environmental sound conversion algorithm to environmental audio. For example, the conversion unit analyzes the environmental audio and converts the audio using environmental sound conversion technology. In this way, the conversion unit applies an appropriate conversion algorithm depending on the category of the audio, thereby improving conversion accuracy. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input audio category data to AI, which then applies an appropriate conversion algorithm.

[0096] During voice conversion, the conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. The conversion unit, for example, optimizes the conversion algorithm based on the user's past conversion results. For example, the conversion unit analyzes past conversion results and adjusts algorithm parameters. The conversion unit can also improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit reduces conversion errors by using past conversion data. The conversion unit can also analyze the user's past conversion results and adjust conversion parameters. For example, the conversion unit optimizes conversion settings based on past conversion history. In this way, the conversion unit improves the accuracy of the conversion by referring to the past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past conversion result data into AI, which can improve the accuracy of the conversion.

[0097] The conversion unit can specifically estimate the user's emotions and specifically determine the conversion priority based on the estimated user emotions. The conversion unit, for example, estimates the user's emotions. For example, the conversion unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The conversion unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the conversion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. Next, the conversion unit determines the conversion priority based on the estimated user emotions. For example, if the user is nervous, it can prioritize important voice conversions. Alternatively, if the user is relaxed, it can process all voice conversions equally. Furthermore, if the user is in a hurry, it can prioritize urgent voice conversions. In this way, the conversion unit can prioritize important voice conversions by determining the conversion priority based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input the user's facial expression data into the generation AI, which may infer emotions and determine conversion priorities based on the emotions.

[0098] During voice conversion, the conversion unit can determine the priority of conversion based on the time of submission of the voice. For example, the conversion unit prioritizes conversion of the most recent voice input. For example, the conversion unit determines the priority of voice based on the submission date and time. The conversion unit can also postpone conversion of older submitted voice input. For example, the conversion unit adjusts the priority of voice based on the order of submission. The conversion unit can also dynamically adjust the priority of conversion based on the time of submission. For example, the conversion unit tracks the time of submission in real time and dynamically changes the priority. This enables efficient conversion by the conversion unit determining the priority of conversion based on the time of submission of the voice. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input submission time data into AI, which can determine the priority of conversion.

[0099] The conversion unit can adjust the order of conversion based on the relevance of the audio during audio conversion. For example, the conversion unit prioritizes conversion of highly relevant audio inputs. For example, the conversion unit determines the priority based on the relevance of the audio content. The conversion unit can also postpone conversion of less relevant audio inputs. For example, the conversion unit adjusts the priority of audio based on the relevance of the topic. The conversion unit can also dynamically adjust the order of conversion based on the relevance of the audio. For example, the conversion unit evaluates the relevance of audio in real time and dynamically changes the conversion order. This enables the conversion unit to adjust the order of conversion based on the relevance of the audio, thereby enabling efficient conversion. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input audio relevance data into AI, which can adjust the order of conversion.

[0100] During speech conversion, the conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise. For example, if the user has specialized knowledge, the conversion unit provides a conversion result that uses a lot of technical terminology. For example, the conversion unit displays the conversion result using technical terms or industry jargon. Furthermore, if the user has general knowledge, the conversion unit can also provide a conversion result that uses less technical terminology. For example, the conversion unit displays the conversion result using general language. Furthermore, the conversion unit can adjust the way the conversion result is presented according to the user's level of expertise. For example, the conversion unit uses simple language for beginners, slightly more technical language for intermediate users, and more technical terminology for advanced users. This allows the conversion unit to provide more appropriate conversion results by adjusting the use of technical terminology in the conversion according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can input the user's level of expertise data into AI, which then adjusts the way the conversion result is presented.

[0101] The output unit can specifically estimate the user's emotion and specifically adjust the output method based on the estimated user's emotion. The output unit, for example, estimates the user's emotion. For example, the output unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The output unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the output unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Next, the output unit adjusts the output method based on the estimated user's emotion. For example, if the user is nervous, the output unit can output voice in a calm tone. If the user is relaxed, the output unit can output voice in a natural tone. Furthermore, if the user is in a hurry, the output unit can output voice quickly. As a result, the output unit can adjust the output method based on the user's emotion, enabling more appropriate voice output. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit may input the user's facial expression data to the generation AI, which may infer the emotion and adjust the output method based on the emotion.

[0102] The output unit can adjust the level of detail of the output based on the importance of the audio at the time of output. For example, the output unit provides detailed output for important audio input. For example, the output unit analyzes the content of the audio in detail and outputs important information with emphasis. The output unit can also provide standard output for general audio input. For example, the output unit analyzes basic features of the audio and provides standard information. The output unit can also provide simplified output for audio input with low importance. For example, the output unit analyzes simple features of the audio and provides simplified information. This allows the output unit to adjust the level of detail of the output based on the importance of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, or without, AI. For example, the output unit can input audio importance data to AI, which can adjust the level of detail of the output.

[0103] The output unit can apply different output algorithms depending on the category of the audio when outputting. The output unit applies a natural language processing algorithm to, for example, conversational audio. For example, the output unit analyzes the conversational audio and outputs the audio using natural language processing technology. The output unit can also apply a music output algorithm to musical audio. For example, the output unit analyzes musical audio and outputs the audio using music output technology. The output unit can also apply an environmental sound output algorithm to environmental audio. For example, the output unit analyzes environmental audio and outputs the audio using environmental sound output technology. In this way, the output unit applies an appropriate output algorithm depending on the category of the audio, thereby improving output accuracy. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input audio category data to AI, which then applies an appropriate output algorithm.

[0104] The output unit can improve the accuracy of the output by referring to the user's past output results at the time of output. The output unit, for example, optimizes the output algorithm based on the user's past output results. For example, the output unit analyzes past output results and adjusts algorithm parameters. The output unit can also improve the accuracy of the output by referring to the user's past output results. For example, the output unit reduces output errors by using past output data. The output unit can also analyze the user's past output results and adjust output parameters. For example, the output unit optimizes output settings based on past output history. In this way, the output unit improves the accuracy of the output by referring to the past output results. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input past output result data to AI, which can improve the accuracy of the output.

[0105] The output unit can specifically estimate the user's emotions and specifically determine output priorities based on the estimated user emotions. The output unit, for example, estimates the user's emotions. For example, the output unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The output unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the output unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. Next, the output unit determines output priorities based on the estimated user emotions. For example, if the user is nervous, important audio outputs can be processed with priority. Alternatively, if the user is relaxed, all audio outputs can be processed equally. Furthermore, if the user is in a hurry, audio outputs with high urgency can be processed with priority. In this way, the output unit can prioritize output based on the user's emotions, thereby outputting important audio with priority. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the output unit may be performed using AI, or may be performed without using AI. For example, the output unit may input the user's facial expression data to the generation AI, which may infer emotions and determine output priorities based on the emotions.

[0106] The output unit can determine the output priority based on the submission time of the audio at the time of output. The output unit, for example, prioritizes the most recent audio input for output. For example, the output unit determines the priority of the audio based on the submission date and time. The output unit can also output audio inputs that were submitted earlier later. For example, the output unit adjusts the priority of the audio based on the submission order. The output unit can also dynamically adjust the output priority based on the submission time. For example, the output unit tracks the submission time in real time and dynamically changes the priority. This enables the output unit to determine the output priority based on the submission time of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input submission time data to AI, and the AI ​​can determine the output priority.

[0107] The output unit can adjust the order of output based on the relevance of the audio at the time of output. For example, the output unit prioritizes and outputs highly relevant audio input. For example, the output unit determines the priority based on the relevance of the audio content. The output unit can also output less relevant audio input later. For example, the output unit adjusts the priority of audio based on the relevance of the topic. The output unit can also dynamically adjust the order of output based on the relevance of the audio. For example, the output unit evaluates the relevance of audio in real time and dynamically changes the output order. This enables the output unit to adjust the output order based on the relevance of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input audio relevance data to AI, and the AI ​​can adjust the order of output.

[0108] The output unit can adjust the use of technical terminology in the output according to the user's level of expertise. For example, if the user has specialized knowledge, the output unit provides an output result that uses a lot of technical terminology. For example, the output unit displays the output result using technical terms or industry jargon. Furthermore, if the user has general knowledge, the output unit can also provide an output result that uses less technical terminology. For example, the output unit displays the output result using general language. Furthermore, the output unit can adjust the way the output result is expressed according to the user's level of expertise. For example, the output unit uses simple language for beginners, slightly more technical language for intermediate users, and more technical terminology for advanced users. This allows the output unit to provide a more appropriate output result by adjusting the use of technical terminology in the output according to the user's level of expertise. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the user's level of expertise data into AI, which then adjusts the way the output result is expressed. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, conversion unit, and output unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can collect the user's voice and facial expression using a microphone or camera of the smart device 14, and estimate the emotion using the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the voice characteristics using, for example, the specific processing unit 290 of the data processing device 12, and the conversion unit can convert the voice using, for example, the specific processing unit 290 of the data processing device 12. The output unit can output the converted voice using, for example, a speaker or headphones of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, conversion unit, and output unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can collect the user's voice and facial expression using a microphone or camera of the smart glasses 214, and estimate the emotion using the specific processing unit 290 of the data processing device 12. The analysis unit can analyze features of the voice using, for example, the specific processing unit 290 of the data processing device 12, and the conversion unit can convert the voice using, for example, the specific processing unit 290 of the data processing device 12. The output unit can output the converted voice using, for example, a speaker or headphones of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, conversion unit, and output unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can collect the user's voice and facial expression using a microphone or camera of the headset-type terminal 314, and estimate the emotion using the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the characteristics of the voice using, for example, the specific processing unit 290 of the data processing device 12, and the conversion unit can convert the voice using, for example, the specific processing unit 290 of the data processing device 12. The output unit can output the converted voice using, for example, a speaker or headphones of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, conversion unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can collect the user's voice and facial expressions using a microphone or camera of the robot 414, and estimate the emotion using the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the voice characteristics using, for example, the specific processing unit 290 of the data processing device 12, and the conversion unit can convert the voice using, for example, the specific processing unit 290 of the data processing device 12. The output unit can output the converted voice using, for example, a speaker or headphones of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] When the user inputs voice, the input unit can analyze the user's past voice input history and select the optimal input method. For example, the input unit can preferentially suggest a voice input method that the user has frequently used in the past. The input unit can also select the most effective input method from the user's past voice input history. Furthermore, the input unit can also suggest the optimal input method for a specific situation based on the user's past voice input history. In this way, the input unit can provide the optimal input method to the user by analyzing the past voice input history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past voice input history to AI, which can select the optimal input method.

[0111] During audio analysis, the analysis unit can apply different analysis algorithms depending on the category of the audio. For example, the analysis unit applies a natural language processing algorithm to conversational audio. For example, the analysis unit analyzes the conversational audio and extracts information using natural language processing technology. The analysis unit can also apply a music analysis algorithm to musical audio. For example, the analysis unit analyzes musical audio and extracts information using music analysis technology. The analysis unit can also apply an environmental sound analysis algorithm to environmental audio. For example, the analysis unit analyzes environmental audio and extracts information using environmental sound analysis technology. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the audio, thereby improving analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio category data into AI, which then applies an appropriate analysis algorithm.

[0112] During voice conversion, the conversion unit can adjust the level of conversion detail based on voice features. For example, the conversion unit adjusts the level of conversion detail based on the pitch or tone of the voice. For example, the conversion unit can analyze the pitch of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also analyze the tone of the voice in detail and adjust the level of conversion detail based on the result. Furthermore, the conversion unit can adjust the level of conversion detail based on the speed of the voice. For example, the conversion unit can analyze the speed of the voice in detail and adjust the level of conversion detail based on the result. The conversion unit can also adjust the level of conversion detail based on the sound quality of the voice. For example, the conversion unit can analyze the sound quality of the voice in detail and adjust the level of conversion detail based on the result. In this way, the conversion unit can adjust the level of conversion detail based on the voice features, thereby enabling more accurate voice conversion. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input voice feature data to AI, and the AI ​​can adjust the level of conversion detail.

[0113] The output unit can adjust the level of detail of the output based on the importance of the audio at the time of output. For example, the output unit provides detailed output for important audio input. For example, the output unit analyzes the content of the audio in detail and outputs important information with emphasis. The output unit can also provide standard output for general audio input. For example, the output unit analyzes basic features of the audio and provides standard information. The output unit can also provide simplified output for audio input with low importance. For example, the output unit analyzes simple features of the audio and provides simplified information. This allows the output unit to adjust the level of detail of the output based on the importance of the audio, thereby enabling efficient output. Some or all of the above-mentioned processing in the output unit may be performed using, or without, AI. For example, the output unit can input audio importance data to AI, which can adjust the level of detail of the output.

[0114] The input unit can specifically estimate the user's emotion and specifically adjust the timing of the voice input based on the estimated user's emotion. For example, the input unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Next, the input unit adjusts the timing of the voice input based on the estimated user's emotion. For example, if the user is nervous, the timing of the voice input can be delayed to give the user time to relax. Also, if the user is relaxed, the timing of the voice input can be advanced to promote smooth conversation. Furthermore, if the user is in a hurry, the timing of the voice input can be optimized to enable a quick response. As a result, the input unit can adjust the timing of the voice input according to the user's emotion, enabling more natural conversation. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit may input the user's facial expression data to the generation AI, which may then estimate the emotion and adjust the timing of the voice input based on the emotion.

[0115] The analysis unit can specifically estimate the user's emotions and specifically adjust the voice analysis presentation method based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. Next, the analysis unit adjusts the voice analysis presentation method based on the estimated user emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. On the other hand, if the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide an analysis result that focuses on the main points. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis presentation method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into a generation AI, which may infer the emotion and adjust the expression method of the analysis based on the emotion.

[0116] The conversion unit can specifically estimate the user's emotion and specifically adjust the voice conversion method based on the estimated user's emotion. For example, the conversion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The conversion unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the conversion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Next, the conversion unit adjusts the voice conversion method based on the estimated user's emotion. For example, if the user is nervous, the voice can be converted to a calm tone. Alternatively, if the user is relaxed, the voice can be converted to a natural tone. Furthermore, if the user is in a hurry, the conversion can be performed quickly and output immediately. This allows the conversion unit to adjust the voice conversion method based on the user's emotion, enabling more appropriate voice conversion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input the user's facial expression data to a generation AI, which may infer the user's emotion and adjust the voice conversion method based on the emotion.

[0117] The output unit can specifically estimate the user's emotion and specifically adjust the output method based on the estimated user's emotion. For example, the output unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The output unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the output unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Next, the output unit adjusts the output method based on the estimated user's emotion. For example, if the user is nervous, the output unit can output voice in a calm tone. If the user is relaxed, the output unit can output voice in a natural tone. Furthermore, if the user is in a hurry, the output unit can output voice quickly. This allows the output unit to adjust the output method based on the user's emotion, thereby enabling more appropriate voice output. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input the user's facial expression data to a generation AI, which may infer the emotion and adjust the output method based on the emotion.

[0118] During voice analysis, the analysis unit can determine the analysis priority based on the time of voice submission. For example, the most recent voice input is given priority for analysis. For example, the analysis unit determines the priority of voice based on the submission date and time. The analysis unit can also postpone analysis of older voice inputs. For example, the analysis unit adjusts the priority of voice based on the order of submission. The analysis unit can also dynamically adjust the analysis priority based on the submission time. For example, the analysis unit tracks the submission time in real time and dynamically changes the priority. This enables the analysis unit to determine the analysis priority based on the submission time, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into AI, which then determines the analysis priority.

[0119] The conversion unit can apply different conversion algorithms depending on the category of the audio during audio conversion. For example, the conversion unit applies a natural language processing algorithm to conversational audio. For example, the conversion unit analyzes the conversational audio and converts the audio using natural language processing technology. The conversion unit can also apply a music conversion algorithm to musical audio. For example, the conversion unit analyzes the musical audio and converts the audio using music conversion technology. The conversion unit can also apply an environmental sound conversion algorithm to environmental audio. For example, the conversion unit analyzes the environmental audio and converts the audio using environmental sound conversion technology. In this way, the conversion unit applies an appropriate conversion algorithm depending on the category of the audio, thereby improving conversion accuracy. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input audio category data into AI, which then applies an appropriate conversion algorithm.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The user inputs voice into the input unit. The user inputs voice using an input device such as a microphone. The input unit can be the built-in microphone of a smartphone or computer, or an external microphone can be connected and used. Step 2: The analysis unit analyzes the voice input by the input unit. The analysis unit analyzes voice characteristics such as pitch, tone, and speed, and uses AI to perform frequency analysis, spectrum analysis, and waveform analysis of the voice. Step 3: The conversion unit converts the voice based on the parameters generated by the analysis unit. The conversion unit performs pitch conversion, speed conversion, and tone conversion of the voice, such as converting a male voice to a female voice. Step 4: The output unit outputs the audio converted by the conversion unit in real time using a speaker, headphones, or a Bluetooth device.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0136] In the 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.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 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.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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 AI 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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, in order to avoid confusion and to 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 input unit for inputting voice; an analysis unit that analyzes the voice input by the input unit; a conversion unit that converts the voice based on the parameters generated by the analysis unit; an output unit that outputs the audio converted by the conversion unit in real time; Equipped with A system characterized by:

2. The analysis unit Analyzes the pitch, tone, and speed characteristics of speech The system of claim 1 .

3. The conversion unit Convert a male voice into a specific female voice The system of claim 1 .

4. The conversion unit Converting to a specific voice for a specific character The system of claim 1 .

5. The output unit Instantly output the converted audio The system of claim 1 .

6. The output unit Convert and output specific audio from a surveillance camera The system of claim 1 .

7. The output unit Converting the voice of an operator to respond to customers The system of claim 1 .

8. The input unit Specifically estimates the user's emotions and specifically adjusts the timing of voice input based on the estimated user emotions. The system of claim 1 .

Images