System

Abstract

An object of a system according to an embodiment is to translate speech between different languages in real time.SOLUTION: A system includes a microphone part, an analysis part, a translation part, and a speaker part. The microphone unit collects voice. The analysis unit analyzes voice data collected by the microphone unit and converts the voice data into text data. The translation unit translates the text data converted by the analysis unit between different languages. The speaker unit outputs a translation result translated by the translation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately address real-time speech translation between different languages, and there is room for improvement.

[0005] The system according to the embodiment aims to translate speech between different languages ​​in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a microphone unit, an analysis unit, a translation unit, and a speaker unit. The microphone unit collects audio. The analysis unit analyzes the audio data collected by the microphone unit and converts it into text data. The translation unit translates the text data converted by the analysis unit between different languages. The speaker unit outputs the translation result obtained by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can translate speech between different languages ​​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 translation system according to an embodiment of the present invention automates a series of processes: collecting, analyzing, translating, and outputting speech. When a user wears earphones and speaks, the built-in microphone collects the speech, and AI analyzes the speech data. The AI ​​translates the collected speech data and outputs the translation result from the earphone speaker. This mechanism allows the user to understand different languages ​​in real time. For example, when a user speaks in English, the built-in microphone in the earphones collects the speech, and the collected speech data is sent to the AI ​​in the earphones. The AI ​​then analyzes the collected speech data and converts it into text data using speech recognition technology. For example, if the speech data "Hello, how are you?" is collected, the AI ​​converts it into text data "Hello, how are you?" The converted text data is then translated by the AI. For example, when translating from English to Japanese, "Hello, how are you?" is translated into "Hello, how are you?" The translation result is output from the earphone speaker. This allows the translation system to understand different languages ​​in real time. For example, when traveling and speaking with locals, using a translation system allows you to overcome language barriers and communicate. It also enables smooth conversations with people who speak different languages ​​in business situations. Furthermore, because translation systems automate the entire process, from collecting voice data to translating and outputting, users do not need to perform any special operations. This makes them easy for anyone to use. For example, even elderly people or those who are not tech-savvy can use the translation function simply by putting on earphones and speaking. In this way, translation systems are convenient tools for understanding different languages ​​in real time and can be used in a variety of situations, including travel, business, and everyday life.

[0029] A translation system according to an embodiment includes a microphone unit, an analysis unit, a translation unit, and a speaker unit. The microphone unit collects speech spoken by a user. For example, the microphone unit collects speech spoken by a user using a high-sensitivity microphone. The microphone unit can also collect clear speech by filtering out ambient noise. The microphone unit can also identify the direction of speech using multiple microphones and collect speech from a specific direction. The analysis unit converts the collected speech data into text data. For example, the analysis unit converts speech data into text data using speech recognition technology. The analysis unit can also improve the quality of the speech data using noise reduction technology. The analysis unit can also understand the context of the speech data and convert it into appropriate text data. The translation unit translates text data between different languages. For example, the translation unit translates text data using machine translation technology. The translation unit can also perform highly accurate translations using a neural network. The translation unit can also appropriately translate technical terms and slang. The speaker unit outputs the translation result. For example, the speaker unit outputs the translation result using a high-quality speaker. The speaker unit can also adjust the sound quality based on the user's hearing characteristics. The speaker unit can also automatically adjust the volume according to the surrounding environmental sounds. This allows the translation system according to the embodiment to automatically perform a series of processes of collecting, analyzing, translating, and outputting speech.

[0030] The microphone unit can analyze ambient environmental sounds and adjust the noise canceling function. The microphone unit analyzes ambient environmental sounds and optimizes the noise canceling function. For example, when the surroundings are noisy, the noise canceling can be strengthened to collect clear audio. When the surroundings are quiet, the noise canceling can be weakened to collect natural audio. Furthermore, when the ambient environmental sounds fluctuate, the noise canceling intensity can be adjusted in real time. This enables clear audio collection by optimizing the noise canceling function according to the ambient environmental sounds. Some or all of the above-described processing in the microphone unit may be performed using, for example, AI, or may be performed without using AI. For example, the microphone unit can input ambient environmental sound data into a generation AI and have the generation AI optimize the noise canceling.

[0031] The microphone unit can adjust the timing of voice collection according to the user's speaking speed. The microphone unit adjusts the timing of voice collection according to the user's speaking speed. For example, if the user speaks quickly, the timing of voice collection can be accelerated to collect all voices. Also, if the user speaks slowly, the timing of voice collection can be slowed to collect natural voices. Also, if the user's speaking speed fluctuates, the timing of voice collection can be adjusted in real time. In this way, by adjusting the timing of voice collection according to the user's speaking speed, all voices can be appropriately collected. Some or all of the above-mentioned processing in the microphone unit may be performed using, for example, AI, or may be performed without using AI. For example, the microphone unit can input the user's speaking speed data to a generation AI and cause the generation AI to adjust the timing of voice collection.

[0032] The microphone unit can prioritize collection of specific keywords based on the content of the user's utterance. The microphone unit prioritizes collection of specific keywords based on the content of the user's utterance. For example, if the user utters a phrase containing a specific keyword, that part can be prioritized and collected. Also, if the user utters a phrase containing an important keyword, that part can be emphasized and collected. The content of the user's utterance can also be analyzed in real time and important keywords can be prioritized and collected. This makes it possible to prioritize collection of important keywords based on the content of the user's utterance, thereby ensuring that important information is not missed. Some or all of the above-described processing in the microphone unit may be performed using, for example, AI, or may be performed without using AI. For example, the microphone unit can input user utterance content data to a generation AI and cause the generation AI to prioritize collection of specific keywords.

[0033] The analysis unit can analyze background sounds of the audio data to improve the accuracy of the speech content. The analysis unit analyzes background sounds of the audio data to improve the accuracy of the speech content. For example, if the background sound is noisy, the noise is removed to clarify the speech content. Also, if the background sound is quiet, the speech content can be analyzed as is. Also, if the background sound fluctuates, the noise can be removed in real time to clarify the speech content. In this way, by analyzing the background sound, the accuracy of the speech content can be improved. 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 background sound data of the audio data to a generation AI and cause the generation AI to improve the accuracy of the speech content.

[0034] The analysis unit can perform text conversion based on attribute information of the speaker of the voice data. The analysis unit performs text conversion taking into account the attribute information of the speaker of the voice data. For example, if the speaker is a child, the analysis unit can convert the speech into simpler expressions. Also, if the speaker is an expert, the analysis unit can convert the speech into expressions that include technical terms. Also, if the speaker is a foreigner, the analysis unit can convert the speech into simple, easy-to-understand expressions. This enables more appropriate text conversion by taking into account the attribute information of the speaker. 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 attribute information data of the speaker into a generation AI and have the generation AI perform text conversion.

[0035] The analysis unit can adjust the timing of text conversion based on the speaking rate of the voice data. The analysis unit adjusts the timing of text conversion based on the speaking rate of the voice data. For example, if the speaking rate is fast, the timing of text conversion is accelerated. Also, if the speaking rate is slow, the timing of text conversion can be slowed. Also, if the speaking rate fluctuates, the timing of text conversion can be adjusted in real time. In this way, by adjusting the timing of text conversion based on the speaking rate, text conversion can be performed at an appropriate timing. 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 speaking rate data of the voice data to the generation AI and cause the generation AI to adjust the timing of text conversion.

[0036] The translation unit can adjust the level of detail of the translation based on the importance of the text data during translation. The translation unit can adjust the level of detail of the translation based on the importance of the text data during translation. For example, important text data can be translated in detail. Also, text data with low importance can be translated concisely. The level of detail of the translation can also be adjusted according to the importance of the text data. In this way, important information can be translated in detail by adjusting the level of detail of the translation based on the importance of the text data. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input importance data of the text data to a generation AI and have the generation AI adjust the level of detail of the translation.

[0037] The translation unit can apply different translation algorithms depending on the category of text data during translation. The translation unit can apply different translation algorithms depending on the category of text data during translation. For example, for business documents, a specialized translation algorithm can be applied. For everyday conversations, a casual translation algorithm can be applied. For technical documents, a technical translation algorithm can be applied. This allows for more appropriate translation by applying different translation algorithms depending on the category of text data. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without using AI. For example, the translation unit can input category data of the text data into a generation AI and cause the generation AI to apply different translation algorithms.

[0038] The translation unit can improve the accuracy of the translation by referring to the user's past translation results during translation. The translation unit can improve the accuracy of the translation by referring to the user's past translation results during translation. For example, the translation accuracy is improved based on translation results used by the user in the past. The translation unit can also analyze the user's past translation history to provide the optimal translation. The translation accuracy can also be improved by referring to the user's past feedback. In this way, the translation accuracy can be improved by referring to the user's past translation results. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the user's past translation result data into the generation AI and have the generation AI improve the translation accuracy.

[0039] The speaker unit can analyze surrounding environmental sounds and automatically adjust the volume when outputting audio. The speaker unit analyzes surrounding environmental sounds and automatically adjusts the volume when outputting audio. For example, if the surroundings are noisy, the volume can be increased to output audio. Also, if the surroundings are quiet, the volume can be decreased to output audio. Also, if the surrounding environmental sounds fluctuate, the volume can be adjusted in real time. In this way, audio can be output at an appropriate volume by automatically adjusting the volume according to the surrounding environmental sounds. Some or all of the above-described processing in the speaker unit may be performed using, for example, AI, or may be performed without using AI. For example, the speaker unit can input surrounding environmental sound data to a generation AI and have the generation AI perform automatic volume adjustment.

[0040] The speaker unit can customize the sound quality based on the user's hearing characteristics when outputting sound. The speaker unit customizes the sound quality based on the user's hearing characteristics when outputting sound. For example, if the user has difficulty hearing high-pitched sounds, the speaker unit can output sound by emphasizing high-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the speaker unit can output sound by emphasizing low-pitched sounds. The speaker unit can also output sound with optimal sound quality based on the user's hearing characteristics. This allows for more appropriate sound output by customizing the sound quality based on the user's hearing characteristics. Some or all of the above-described processing in the speaker unit may be performed using, for example, AI, or may be performed without using AI. For example, the speaker unit can input the user's hearing characteristic data into a generation AI and have the generation AI customize the sound quality.

[0041] The speaker unit can adjust the emphasized parts of the voice when outputting the voice based on the importance of the translation result. The speaker unit can adjust the emphasized parts of the voice when outputting the voice based on the importance of the translation result. For example, the speaker unit can emphasize important translation results when outputting the voice. It can also output translation results with low importance in normal voice. It can also adjust the emphasized parts of the voice according to the importance of the translation result. In this way, important information can be emphasized and output by adjusting the emphasized parts of the voice based on the importance of the translation result. Some or all of the above-mentioned processing in the speaker unit may be performed using, for example, AI, or may be performed without using AI. For example, the speaker unit can input importance data of the translation result to a generation AI and have the generation AI adjust the emphasized parts of the voice.

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

[0043] The translation system can further include a location information acquisition unit that acquires the user's location information and adjusts the context of the translation based on the location information. For example, if the user is in a specific country, the system can provide a translation based on the culture and customs of that country. Also, if the user is in a specific location (e.g., an airport or a restaurant), the system can provide a translation appropriate for that location. Furthermore, if the user is on the move, the system can update the location information in real time and provide an appropriate translation. This enables translation that is more appropriate to the context based on the user's location information.

[0044] The translation system can analyze the user's past translation history and improve the accuracy of the translation based on that history. For example, it can prioritize specific phrases and expressions that the user has used in the past. It can also refer to translation results corrected by the user in the past to improve the accuracy of translations in similar situations. Furthermore, it can reflect the user's past feedback to continuously improve the quality of the translation. This makes it possible to utilize the user's past translation history to achieve more accurate translations.

[0045] The translation system can emphasize specific keywords in the translation based on the content of the user's utterance. For example, if the user's utterance contains an important keyword, that part can be emphasized and reflected in the translation results. Also, if the user repeatedly uses a specific keyword, that part can be translated with particular attention. Furthermore, the system can analyze the user's utterance in real time and emphasize important keywords in the translation. This allows for more appropriate communication by translating with emphasis on important information based on the user's utterance.

[0046] The translation system can adjust the timing of translation based on the user's speaking speed. For example, if the user speaks quickly, the translation timing can be accelerated to properly translate all speech. Alternatively, if the user speaks slowly, the translation timing can be slowed down to translate at a natural pace. Furthermore, if the user's speaking speed fluctuates, the translation timing can be adjusted in real time. This allows all speech to be properly translated by adjusting the translation timing according to the user's speaking speed.

[0047] The translation system can apply a translation algorithm according to a specific category based on the content of the user's utterance. For example, a specialized translation algorithm can be applied to business documents. A casual translation algorithm can also be applied to everyday conversations. Furthermore, a technical translation algorithm can be applied to technical documents. This allows for more appropriate translation by applying the appropriate translation algorithm based on the content of the user's utterance.

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

[0049] Step 1: The microphone unit collects the voice spoken by the user. For example, the microphone unit can collect voice using a high-sensitivity microphone and eliminate surrounding environmental noise to collect clear voice. It can also use multiple microphones to identify the direction of the voice and collect voice from a specific direction. Step 2: The analyzer converts the collected voice data into text data. For example, the analyzer can use voice recognition technology to convert the voice data into text data and noise reduction technology to improve the quality of the voice data. The analyzer can also understand the context of the voice data and convert it into appropriate text data. Step 3: The translation unit translates the text data converted by the analysis unit between different languages. For example, the translation unit can translate the text data using machine translation technology and neural networks to achieve high-precision translation. The translation unit can also properly translate technical terms and slang. Step 4: The speaker unit outputs the translation results from the translation unit. For example, the speaker unit can output the translation results using a high-quality speaker and adjust the sound quality based on the user's hearing characteristics. The speaker unit can also automatically adjust the volume according to the surrounding environmental sounds.

[0050] (Example 2) A translation system according to an embodiment of the present invention automates a series of processes: collecting, analyzing, translating, and outputting speech. When a user wears earphones and speaks, the built-in microphone collects the speech, and AI analyzes the speech data. The AI ​​translates the collected speech data and outputs the translation result from the earphone speaker. This mechanism allows the user to understand different languages ​​in real time. For example, when a user speaks in English, the built-in microphone in the earphones collects the speech, and the collected speech data is sent to the AI ​​in the earphones. The AI ​​then analyzes the collected speech data and converts it into text data using speech recognition technology. For example, if the speech data "Hello, how are you?" is collected, the AI ​​converts it into text data "Hello, how are you?" The converted text data is then translated by the AI. For example, when translating from English to Japanese, "Hello, how are you?" is translated into "Hello, how are you?" The translation result is output from the earphone speaker. This allows the translation system to understand different languages ​​in real time. For example, when traveling and speaking with locals, using a translation system allows you to overcome language barriers and communicate. It also enables smooth conversations with people who speak different languages ​​in business situations. Furthermore, because translation systems automate the entire process, from collecting voice data to translating and outputting, users do not need to perform any special operations. This makes them easy for anyone to use. For example, even elderly people or those who are not tech-savvy can use the translation function simply by putting on earphones and speaking. In this way, translation systems are convenient tools for understanding different languages ​​in real time and can be used in a variety of situations, including travel, business, and everyday life.

[0051] A translation system according to an embodiment includes a microphone unit, an analysis unit, a translation unit, and a speaker unit. The microphone unit collects speech spoken by a user. For example, the microphone unit collects speech spoken by a user using a high-sensitivity microphone. The microphone unit can also collect clear speech by filtering out ambient noise. The microphone unit can also identify the direction of speech using multiple microphones and collect speech from a specific direction. The analysis unit converts the collected speech data into text data. For example, the analysis unit converts speech data into text data using speech recognition technology. The analysis unit can also improve the quality of the speech data using noise reduction technology. The analysis unit can also understand the context of the speech data and convert it into appropriate text data. The translation unit translates text data between different languages. For example, the translation unit translates text data using machine translation technology. The translation unit can also perform highly accurate translations using a neural network. The translation unit can also appropriately translate technical terms and slang. The speaker unit outputs the translation result. For example, the speaker unit outputs the translation result using a high-quality speaker. The speaker unit can also adjust the sound quality based on the user's hearing characteristics. The speaker unit can also automatically adjust the volume according to the surrounding environmental sounds. This allows the translation system according to the embodiment to automatically perform a series of processes of collecting, analyzing, translating, and outputting speech.

[0052] The translation system includes a microphone unit that analyzes a user's emotions and adjusts the sensitivity of voice collection based on the analyzed user emotions. The microphone unit estimates the user's emotions and adjusts the sensitivity of voice collection based on the estimated user emotions. For example, if the user is nervous, the sensitivity is increased to collect even quieter voices. Alternatively, if the user is relaxed, the sensitivity can be set to normal to collect natural voices. Alternatively, if the user is excited, the sensitivity can be decreased to collect even louder voices clearly. This allows for more appropriate voice collection by adjusting the sensitivity of voice collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation 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 microphone unit may be performed using AI, for example, or without AI. For example, the microphone unit may input the user's voice data into the generation AI and cause the generation AI to estimate the emotion.

[0053] The microphone unit can analyze ambient environmental sounds and adjust the noise canceling function. The microphone unit analyzes ambient environmental sounds and optimizes the noise canceling function. For example, when the surroundings are noisy, the noise canceling can be strengthened to collect clear audio. When the surroundings are quiet, the noise canceling can be weakened to collect natural audio. Furthermore, when the ambient environmental sounds fluctuate, the noise canceling intensity can be adjusted in real time. This enables clear audio collection by optimizing the noise canceling function according to the ambient environmental sounds. Some or all of the above-described processing in the microphone unit may be performed using, for example, AI, or may be performed without using AI. For example, the microphone unit can input ambient environmental sound data into a generation AI and have the generation AI optimize the noise canceling.

[0054] The microphone unit can adjust the timing of voice collection according to the user's speaking speed. The microphone unit adjusts the timing of voice collection according to the user's speaking speed. For example, if the user speaks quickly, the timing of voice collection can be accelerated to collect all voices. Also, if the user speaks slowly, the timing of voice collection can be slowed to collect natural voices. Also, if the user's speaking speed fluctuates, the timing of voice collection can be adjusted in real time. In this way, by adjusting the timing of voice collection according to the user's speaking speed, all voices can be appropriately collected. Some or all of the above-mentioned processing in the microphone unit may be performed using, for example, AI, or may be performed without using AI. For example, the microphone unit can input the user's speaking speed data to a generation AI and cause the generation AI to adjust the timing of voice collection.

[0055] The microphone unit can prioritize collection of specific keywords based on the content of the user's utterance. The microphone unit prioritizes collection of specific keywords based on the content of the user's utterance. For example, if the user utters a phrase containing a specific keyword, that part can be prioritized and collected. Also, if the user utters a phrase containing an important keyword, that part can be emphasized and collected. The content of the user's utterance can also be analyzed in real time and important keywords can be prioritized and collected. This makes it possible to prioritize collection of important keywords based on the content of the user's utterance, thereby ensuring that important information is not missed. Some or all of the above-described processing in the microphone unit may be performed using, for example, AI, or may be performed without using AI. For example, the microphone unit can input user utterance content data to a generation AI and cause the generation AI to prioritize collection of specific keywords.

[0056] The analysis unit can analyze the user's emotions and adjust the expression method for text conversion based on the analyzed user's emotions. The analysis unit can estimate the user's emotions and adjust the expression method for text conversion based on the estimated user's emotions. For example, if the user is nervous, the expression can be converted to a concise and easy-to-understand expression. Also, if the user is relaxed, the expression can be converted to a detailed expression. Also, if the user is excited, the expression can be converted to an expression that emphasizes the emotion. This enables more appropriate text conversion by adjusting the expression method for text conversion according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can 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-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression method for text conversion.

[0057] The analysis unit can analyze background sounds of the audio data to improve the accuracy of the speech content. The analysis unit analyzes background sounds of the audio data to improve the accuracy of the speech content. For example, if the background sound is noisy, the noise is removed to clarify the speech content. Also, if the background sound is quiet, the speech content can be analyzed as is. Also, if the background sound fluctuates, the noise can be removed in real time to clarify the speech content. In this way, by analyzing the background sound, the accuracy of the speech content can be improved. 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 background sound data of the audio data to a generation AI and cause the generation AI to improve the accuracy of the speech content.

[0058] The analysis unit can perform text conversion based on attribute information of the speaker of the voice data. The analysis unit performs text conversion taking into account the attribute information of the speaker of the voice data. For example, if the speaker is a child, the analysis unit can convert the speech into simpler expressions. Also, if the speaker is an expert, the analysis unit can convert the speech into expressions that include technical terms. Also, if the speaker is a foreigner, the analysis unit can convert the speech into simple, easy-to-understand expressions. This enables more appropriate text conversion by taking into account the attribute information of the speaker. 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 attribute information data of the speaker into a generation AI and have the generation AI perform text conversion.

[0059] The analysis unit can adjust the timing of text conversion based on the speaking rate of the voice data. The analysis unit adjusts the timing of text conversion based on the speaking rate of the voice data. For example, if the speaking rate is fast, the timing of text conversion is accelerated. Also, if the speaking rate is slow, the timing of text conversion can be slowed. Also, if the speaking rate fluctuates, the timing of text conversion can be adjusted in real time. In this way, by adjusting the timing of text conversion based on the speaking rate, text conversion can be performed at an appropriate timing. 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 speaking rate data of the voice data to the generation AI and cause the generation AI to adjust the timing of text conversion.

[0060] The translation unit can analyze the user's emotions and adjust the translation expression based on the analyzed user's emotions. The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is nervous, the translation can be simplified and easy to understand. Alternatively, if the user is relaxed, the translation can be detailed. Alternatively, if the user is excited, the translation can be emphasized. This allows for more appropriate translation by adjusting the translation expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 translation unit can be performed using AI, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0061] The translation unit can adjust the level of detail of the translation based on the importance of the text data during translation. The translation unit can adjust the level of detail of the translation based on the importance of the text data during translation. For example, important text data can be translated in detail. Also, text data with low importance can be translated concisely. The level of detail of the translation can also be adjusted according to the importance of the text data. In this way, important information can be translated in detail by adjusting the level of detail of the translation based on the importance of the text data. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input importance data of the text data to a generation AI and have the generation AI adjust the level of detail of the translation.

[0062] The translation unit can apply different translation algorithms depending on the category of text data during translation. The translation unit can apply different translation algorithms depending on the category of text data during translation. For example, for business documents, a specialized translation algorithm can be applied. For everyday conversations, a casual translation algorithm can be applied. For technical documents, a technical translation algorithm can be applied. This allows for more appropriate translation by applying different translation algorithms depending on the category of text data. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without using AI. For example, the translation unit can input category data of the text data into a generation AI and cause the generation AI to apply different translation algorithms.

[0063] The translation unit can improve the accuracy of the translation by referring to the user's past translation results during translation. The translation unit can improve the accuracy of the translation by referring to the user's past translation results during translation. For example, the translation accuracy is improved based on translation results used by the user in the past. The translation unit can also analyze the user's past translation history to provide the optimal translation. The translation accuracy can also be improved by referring to the user's past feedback. In this way, the translation accuracy can be improved by referring to the user's past translation results. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the user's past translation result data into the generation AI and have the generation AI improve the translation accuracy.

[0064] The speaker unit can analyze the user's emotions and adjust the tone of the audio output based on the analyzed user's emotions. The speaker unit can estimate the user's emotions and adjust the tone of the audio output based on the estimated user's emotions. For example, if the user is nervous, the speaker unit can output audio in a calm tone. If the user is relaxed, the speaker unit can output audio in a bright tone. If the user is excited, the speaker unit can output audio in a powerful tone. This allows for more appropriate audio output by adjusting the tone of the audio output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 speaker unit can be performed using, for example, AI, or without AI. For example, the speaker unit can input user's emotion data into the generation AI and have the generation AI adjust the tone of the audio output.

[0065] The speaker unit can analyze surrounding environmental sounds and automatically adjust the volume when outputting audio. The speaker unit analyzes surrounding environmental sounds and automatically adjusts the volume when outputting audio. For example, if the surroundings are noisy, the volume can be increased to output audio. Also, if the surroundings are quiet, the volume can be decreased to output audio. Also, if the surrounding environmental sounds fluctuate, the volume can be adjusted in real time. In this way, audio can be output at an appropriate volume by automatically adjusting the volume according to the surrounding environmental sounds. Some or all of the above-described processing in the speaker unit may be performed using, for example, AI, or may be performed without using AI. For example, the speaker unit can input surrounding environmental sound data to a generation AI and have the generation AI perform automatic volume adjustment.

[0066] The speaker unit can customize the sound quality based on the user's hearing characteristics when outputting sound. The speaker unit customizes the sound quality based on the user's hearing characteristics when outputting sound. For example, if the user has difficulty hearing high-pitched sounds, the speaker unit can output sound by emphasizing high-pitched sounds. Also, if the user has difficulty hearing low-pitched sounds, the speaker unit can output sound by emphasizing low-pitched sounds. The speaker unit can also output sound with optimal sound quality based on the user's hearing characteristics. This allows for more appropriate sound output by customizing the sound quality based on the user's hearing characteristics. Some or all of the above-described processing in the speaker unit may be performed using, for example, AI, or may be performed without using AI. For example, the speaker unit can input the user's hearing characteristic data into a generation AI and have the generation AI customize the sound quality.

[0067] The speaker unit can adjust the emphasized parts of the voice when outputting the voice based on the importance of the translation result. The speaker unit can adjust the emphasized parts of the voice when outputting the voice based on the importance of the translation result. For example, the speaker unit can emphasize important translation results when outputting the voice. It can also output translation results with low importance in normal voice. It can also adjust the emphasized parts of the voice according to the importance of the translation result. In this way, important information can be emphasized and output by adjusting the emphasized parts of the voice based on the importance of the translation result. Some or all of the above-mentioned processing in the speaker unit may be performed using, for example, AI, or may be performed without using AI. For example, the speaker unit can input importance data of the translation result to a generation AI and have the generation AI adjust the emphasized parts of the voice.

[0068] The speaker unit can analyze the user's emotions and determine the priority of audio output based on the analyzed user's emotions. The speaker unit can estimate the user's emotions and determine the priority of audio output based on the estimated user's emotions. For example, if the user is nervous, important audio can be output preferentially. Also, if the user is relaxed, all audio can be output equally. Also, if the user is excited, audio with strong emotions can be output preferentially. This allows important information to be output preferentially by determining the priority of audio output according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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-mentioned processing in the speaker unit can be performed using, for example, AI, or without AI. For example, the speaker unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of audio output. === Hard Collateral 1-1 === Each of the multiple elements, including the microphone unit, analysis unit, translation unit, and speaker unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the microphone unit is implemented by the microphone 38B of the smart device 14 and collects the user's voice. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and converts the collected voice data into text data. The translation unit is implemented by the specific processing unit 290 of the data processing device 12 and translates the text data into a different language. The speaker unit is implemented by the speaker 40B of the smart device 14 and outputs the translation result. The microphone unit may be implemented, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, translation unit, and speaker unit may be implemented, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned microphone unit, analysis unit, translation unit, and speaker unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the microphone unit is realized by the microphone 238 of the smart glasses 214 and collects the user's voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and converts the collected voice data into text data. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the text data into a different language. The speaker unit is realized by the speaker 240 of the smart glasses 214 and outputs the translation result. The microphone unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, translation unit, and speaker unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned microphone unit, analysis unit, translation unit, and speaker unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the microphone unit is realized by the microphone 238 of the headset-type terminal 314 and collects the user's voice. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and converts the collected voice data into text data. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the text data into a different language. The speaker unit is realized by the speaker 240 of the headset-type terminal 314 and outputs the translation result. The microphone unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, translation unit, and speaker unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the microphone unit, analysis unit, translation unit, and speaker unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the microphone unit is realized by the microphone 238 of the robot 414 and collects the user's voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and converts the collected voice data into text data. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the text data into a different language. The speaker unit is realized by the speaker 240 of the robot 414 and outputs the translation result. The microphone unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the analysis unit, translation unit, and speaker unit may be realized, for example, by the control unit 46A of the robot 414.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The translation system can further include a location information acquisition unit that acquires the user's location information and adjusts the context of the translation based on the location information. For example, if the user is in a specific country, the system can provide a translation based on the culture and customs of that country. Also, if the user is in a specific location (e.g., an airport or a restaurant), the system can provide a translation appropriate for that location. Furthermore, if the user is on the move, the system can update the location information in real time and provide an appropriate translation. This enables translation that is more appropriate to the context based on the user's location information.

[0071] The translation system can estimate the user's emotions and adjust the tone of the translation based on the estimated emotions. For example, if the user is angry, the translation can be provided in a calm and subdued tone. If the user is sad, the translation can be provided in a gentle tone. Furthermore, if the user is happy, the translation can be provided in a bright tone. This allows for more appropriate communication by providing translations in a tone that matches the user's emotions.

[0072] The translation system can analyze the user's past translation history and improve the accuracy of the translation based on that history. For example, it can prioritize specific phrases and expressions that the user has used in the past. It can also refer to translation results corrected by the user in the past to improve the accuracy of translations in similar situations. Furthermore, it can reflect the user's past feedback to continuously improve the quality of the translation. This makes it possible to utilize the user's past translation history to achieve more accurate translations.

[0073] The translation system can estimate the user's emotions and adjust the translation speed based on the estimated emotions. For example, if the user is in a hurry, the translation speed can be increased to provide results quickly. Alternatively, if the user is relaxed, the translation speed can be set to normal to provide results at a natural pace. Furthermore, if the user is excited, the translation speed can be adjusted to provide results at an appropriate time. This allows for more appropriate service by providing translations at a speed that corresponds to the user's emotions.

[0074] The translation system can emphasize specific keywords in the translation based on the content of the user's utterance. For example, if the user's utterance contains an important keyword, that part can be emphasized and reflected in the translation results. Also, if the user repeatedly uses a specific keyword, that part can be translated with particular attention. Furthermore, the system can analyze the user's utterance in real time and emphasize important keywords in the translation. This allows for more appropriate communication by translating with emphasis on important information based on the user's utterance.

[0075] The translation system can estimate the user's emotions and adjust the translation expression based on the estimated emotions. For example, if the user is nervous, the translation can be made simple and easy to understand. If the user is relaxed, the translation can be made detailed. Furthermore, if the user is excited, the translation can be made to emphasize the emotion. This allows for more appropriate translation by adjusting the translation expression according to the user's emotions.

[0076] The translation system can adjust the timing of translation based on the user's speaking speed. For example, if the user speaks quickly, the translation timing can be accelerated to properly translate all speech. Alternatively, if the user speaks slowly, the translation timing can be slowed down to translate at a natural pace. Furthermore, if the user's speaking speed fluctuates, the translation timing can be adjusted in real time. This allows all speech to be properly translated by adjusting the translation timing according to the user's speaking speed.

[0077] The translation system can estimate the user's emotions and determine the translation priority based on the estimated emotions. For example, if the user is nervous, important translation results can be provided preferentially. Alternatively, if the user is relaxed, all translation results can be provided equally. Furthermore, if the user is excited, parts with strong emotions can be translated preferentially. In this way, by determining the translation priority according to the user's emotions, important information can be provided preferentially.

[0078] The translation system can apply a translation algorithm according to a specific category based on the content of the user's utterance. For example, a specialized translation algorithm can be applied to business documents. A casual translation algorithm can also be applied to everyday conversations. Furthermore, a technical translation algorithm can be applied to technical documents. This allows for more appropriate translation by applying the appropriate translation algorithm based on the content of the user's utterance.

[0079] The translation system can estimate the user's emotions and provide translation feedback based on the estimated emotions. For example, if the user is dissatisfied, it can provide suggestions for improving the translation result. Alternatively, if the user is satisfied, it can provide the translation result as is. Furthermore, if the user has questions, it can provide additional explanations. This allows for more appropriate translation services by providing feedback according to the user's emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The microphone unit collects the voice spoken by the user. For example, the microphone unit can collect voice using a high-sensitivity microphone and eliminate surrounding environmental noise to collect clear voice. It can also use multiple microphones to identify the direction of the voice and collect voice from a specific direction. Step 2: The analyzer converts the collected voice data into text data. For example, the analyzer can use voice recognition technology to convert the voice data into text data and noise reduction technology to improve the quality of the voice data. The analyzer can also understand the context of the voice data and convert it into appropriate text data. Step 3: The translation unit translates the text data converted by the analysis unit between different languages. For example, the translation unit can translate the text data using machine translation technology and neural networks to achieve high-precision translation. The translation unit can also properly translate technical terms and slang. Step 4: The speaker unit outputs the translation results from the translation unit. For example, the speaker unit can output the translation results using a high-quality speaker and adjust the sound quality based on the user's hearing characteristics. The speaker unit can also automatically adjust the volume according to the surrounding environmental sounds.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0112] 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.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] 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.

[0153] [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a microphone unit for collecting sound; an analysis unit that analyzes the voice data collected by the microphone unit and converts it into text data; a translation unit that translates the text data converted by the analysis unit between different languages; a speaker unit that outputs the translation result obtained by the translation unit; Equipped with A system characterized by:

2. The microphone unit is Analyzing user emotions and adjusting the sensitivity of voice collection based on the analyzed user emotions 2. The system of claim 1.

3. The microphone unit is Analyzes surrounding environmental sounds and adjusts noise canceling function 2. The system of claim 1.

4. The microphone unit is Adjusting the timing of voice collection according to the user's speaking rate 2. The system of claim 1.

5. The microphone unit is Prioritize collection of specific keywords based on user utterances 2. The system of claim 1.

6. The analysis unit Analyze user sentiment and adjust the way text is converted based on the analyzed sentiment.

2. The system of claim 1.

7. The analysis unit Analyze background sounds in voice data to improve the accuracy of what is being said 2. The system of claim 1.

8. The analysis unit Converts voice data into text based on speaker attributes 2. The system of claim 1.

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