system

Abstract

The system according to the embodiment aims to translate different languages ​​instantly while canceling noise. [Solution] A system according to an embodiment includes a collection unit, a voice processing unit, a translation unit, and a voice output unit. The collection unit collects external voice. The voice processing unit performs noise cancellation on the voice collected by the collection unit. The translation unit translates the voice from which noise has been canceled by the voice processing unit. The voice output unit provides the voice translated by the translation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to communicate smoothly between people who speak different languages ​​and being susceptible to noise.

[0005] The system according to the embodiment aims to translate different languages ​​instantly while canceling noise. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a voice processing unit, a translation unit, and a voice output unit. The collection unit collects external voice. The voice processing unit performs noise cancellation on the voice collected by the collection unit. The translation unit translates the voice from which noise has been canceled by the voice processing unit. The voice output unit provides the voice translated by the translation unit. [Effects of the Invention]

[0007] The system according to this embodiment can instantly translate between different languages ​​while canceling noise. [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) An embodiment of the present invention provides a voice translation system that cancels external noise using active noise cancellation technology and immediately provides the user with translated audio. This voice translation system collects external noise with a microphone and processes the collected audio using active noise cancellation technology to cancel noise. The noise-free audio is then input to a translation engine to immediately generate translated audio. Finally, the generated translated audio is provided to the user. For example, the voice translation system significantly reduces communication barriers in international environments and among people with diverse linguistic backgrounds, enabling more natural and smoother conversations. For instance, the voice translation system is envisioned for use in situations such as conferences, business meetings, and travel. By translating external audio in real time and providing it to the user, the voice translation system facilitates communication between people who speak different languages. For example, the voice translation system collects external noise with a high-precision microphone and removes noise using active noise cancellation technology. Next, the noise-free audio is input to a high-performance translation engine to immediately generate translated audio. Finally, the generated translated audio is provided to the user with clear sound quality. This allows voice translation systems to provide users with an experience as if the other person were speaking in their own language. As a result, voice translation systems can significantly reduce communication barriers in international settings and between people with diverse linguistic backgrounds, enabling more natural and smoother conversations.

[0029] A speech translation system according to an embodiment includes a collection unit, a speech processing unit, a translation unit, and a speech output unit. The collection unit collects external speech. The collection unit can collect external speech, such as environmental sounds, conversations, and music. The collection unit collects external speech using, for example, a high-sensitivity microphone. The speech processing unit performs noise cancellation on the speech collected by the collection unit. The speech processing unit effectively cancels external noise using, for example, active noise canceling technology. Active noise canceling technology can be realized using methods such as a feedforward method, a feedback method, or a hybrid method. For example, the feedforward method collects external noise using a microphone and cancels the noise with antiphase sound waves. The feedback method collects noise inside the ear using a microphone and cancels the noise with antiphase sound waves. The hybrid method combines both the feedforward method and the feedback method to cancel noise. The translation unit translates the speech from which noise has been canceled by the speech processing unit. The translation unit instantly translates the speech using, for example, a real-time translation engine. The translation engine may be, for example, Google (registered trademark) Translate, Microsoft (registered trademark) Translator, or a proprietary engine. The audio output unit provides the audio translated by the translation unit. The audio output unit provides the translated audio to the user using, for example, a speaker, earphones, or a headset. As a result, the speech translation system according to the embodiment collects external audio, cancels noise, translates, and provides the audio, thereby providing the user with a natural and smooth conversation experience.

[0030] The sound collection unit is equipped with microphones. These microphones may include, for example, condenser microphones, dynamic microphones, or directional microphones. Condenser microphones offer high sensitivity and cover a wide frequency range, enabling high-precision collection of ambient sounds and conversations. Dynamic microphones are highly durable and can capture clear audio even in noisy environments. Directional microphones prioritize sound collection from specific directions, effectively eliminating ambient noise. For example, the sound collection unit can accurately capture conversations in a conference room using a condenser microphone. It can also capture clear audio in noisy outdoor environments using a dynamic microphone. Furthermore, the sound collection unit can prioritize sound collection from specific directions using a directional microphone, effectively eliminating ambient noise. This allows the sound collection unit to effectively capture external sounds.

[0031] The audio processing unit incorporates active noise cancellation technology. The audio processing unit incorporates active noise cancellation technologies such as feedforward, feedback, and hybrid methods. The feedforward method collects external noise with a microphone and cancels it with sound waves of the opposite phase. For example, the audio processing unit can effectively cancel external noise using the feedforward method. The feedback method collects noise from inside the ear with a microphone and cancels it with sound waves of the opposite phase. For example, the audio processing unit can effectively cancel noise from inside the ear using the feedback method. The hybrid method combines both the feedforward and feedback methods to cancel noise. For example, the audio processing unit can effectively cancel both external noise and noise from inside the ear using the hybrid method. This allows the audio processing unit to effectively cancel external noise.

[0032] The translation unit includes a translation engine. The translation unit includes a translation engine such as Google Translate, Microsoft Translator, or a proprietary engine. Google Translate supports multiple languages ​​and can provide highly accurate translations. Microsoft Translator has a real-time translation function and can provide instantly translated audio. The proprietary engine can provide a translation function customized for specific applications and needs. For example, the translation unit can use Google Translate to provide highly accurate translations in multiple languages. The translation unit can also use Microsoft Translator to provide instantly translated audio. The translation unit can also use a proprietary engine to provide a customized translation function tailored for specific applications and needs. This allows the translation unit to instantly translate collected audio.

[0033] The audio output unit includes a speaker. The audio output unit includes a speaker such as a built-in speaker, an external speaker, or a Bluetooth (registered trademark) speaker. The built-in speaker is built into the device and can easily output audio. The external speaker can output high-quality audio at a high volume. The Bluetooth speaker can be connected wirelessly and can be freely positioned. For example, the audio output unit can easily output audio using the built-in speaker. Furthermore, the audio output unit can output high-quality audio at a high volume using an external speaker. Furthermore, the audio output unit can be connected wirelessly using a Bluetooth speaker and can be freely positioned to output audio. This allows the audio output unit to provide the translated audio to the user.

[0034] The collection unit can analyze surrounding environmental sounds during collection and select an optimal collection method. For example, the collection unit analyzes surrounding environmental sounds in real time and selects an optimal collection method. Surrounding environmental sounds include, for example, traffic sounds, wind sounds, and people talking. For example, when the surroundings are noisy, the collection unit can use a directional microphone to collect sounds from a specific direction. A directional microphone can preferentially collect sounds from a specific direction and effectively remove ambient noise. For example, the collection unit can use a directional microphone to collect the voice of a specific speaker in a conference room. Furthermore, when the surroundings are quiet, the collection unit can use an omnidirectional microphone to collect sounds from a wide range. An omnidirectional microphone can evenly collect sounds from all directions. For example, the collection unit can use an omnidirectional microphone to collect outdoor natural sounds. Furthermore, when the surrounding environmental sounds fluctuate, the collection unit can analyze the environmental sounds in real time and dynamically select an optimal collection method. For example, the collection unit can switch between a directional microphone and an omnidirectional microphone depending on the fluctuations in the environmental sounds. This allows the data collection unit to select the optimal collection method by analyzing the surrounding ambient sounds.

[0035] During collection, the collection unit can prioritize collection of sounds in a specific frequency band. For example, the collection unit prioritizes collection of a frequency band that mainly contains human voices and filters out other sounds. Human voices are typically included in a frequency band from 300 Hz to 3400 Hz. For example, the collection unit can prioritize collection of this frequency band and filter out sounds in other frequency bands. The collection unit can also prioritize collection of a corresponding frequency band based on the sound characteristics of a specific language. For example, the collection unit can prioritize collection of a specific frequency band based on the sound characteristics of a specific language and filter out other sounds. Furthermore, the collection unit can prioritize collection of a specific sound (e.g., an alarm sound) specified by the user. For example, the collection unit can prioritize collection of a specific sound specified by the user and filter out other sounds. In this way, the collection unit can effectively collect required sounds by prioritize collection of sounds in a specific frequency band.

[0036] The data collection unit can prioritize collecting relevant audio based on the user's geographical location information during data collection. The data collection unit obtains the user's geographical location information using, for example, GPS data and Wi-Fi location information. GPS data can pinpoint the user's current location with high accuracy. Wi-Fi location information can pinpoint the user's location based on the location information of Wi-Fi access points. For example, if the user is in a specific location, the data collection unit can prioritize collecting audio related to that location. For example, if the user is in a tourist destination, the data collection unit can prioritize collecting audio related to that tourist destination. Furthermore, if the user is on the move, the data collection unit can prioritize collecting highly relevant audio by considering the geographical location information of the destination. For example, if the user is in a moving vehicle, the data collection unit can prioritize collecting relevant audio based on the geographical location information of the destination. In addition, if the user is participating in a specific event, the data collection unit can prioritize collecting audio related to that event. For example, if the user is attending a concert, the data collection unit can prioritize collecting audio related to that concert. In this way, the data collection unit can prioritize collecting highly relevant audio by considering the user's geographical location information.

[0037] The data collection unit can analyze a user's social media activity and collect relevant audio during the collection process. For example, the data collection unit can analyze the user's social media posts and followers. Social media activity analysis includes, for example, prioritizing the collection of audio related to a specific topic if the user posts about that topic. The data collection unit can also analyze the posts of accounts the user follows on social media and collect relevant audio. For example, the data collection unit can analyze the posts of accounts the user follows and prioritize the collection of audio related to that content. Furthermore, the data collection unit can prioritize the collection of audio related to events the user is participating in on social media. For example, the data collection unit can prioritize the collection of audio related to events the user is participating in on social media. In this way, the data collection unit can effectively collect relevant audio by analyzing the user's social media activity.

[0038] The audio processing unit can learn specific noise patterns during noise cancellation to improve the accuracy of noise cancellation. For example, the audio processing unit can learn surrounding environmental sounds in real time and identify specific noise patterns to improve the accuracy of cancellation. Specific noise patterns include, for example, traffic noise, wind noise, and people talking. For example, the audio processing unit can learn surrounding environmental sounds in real time and identify specific noise patterns to improve the accuracy of cancellation. The audio processing unit can also analyze a user's past noise cancellation history and learn specific noise patterns to improve the accuracy of cancellation. For example, the audio processing unit can analyze a user's past noise cancellation history and learn specific noise patterns to improve the accuracy of cancellation. Furthermore, the audio processing unit can learn noise patterns in a specific location (e.g., an office or a cafe) and perform noise cancellation appropriate for that location. For example, the audio processing unit can learn noise patterns in a specific location and perform noise cancellation appropriate for that location. In this way, the audio processing unit improves the accuracy of noise cancellation by learning specific noise patterns.

[0039] The audio processing unit can detect the direction of sound during noise cancellation and prioritize processing of sound from a specific direction. The audio processing unit can detect the direction of sound using, for example, a microphone array or sound source localization technology. A microphone array is a technology that uses multiple microphones to detect the direction of sound arrival. For example, the audio processing unit can use a microphone array to detect the direction of sound and prioritize processing of sound from a specific direction. Sound source localization technology is a technology that detects the direction of sound using differences in arrival time or intensity. For example, the audio processing unit can use sound source localization technology to detect the direction of sound and prioritize processing of sound from a specific direction. For example, the audio processing unit can prioritize processing sound from the direction the user is facing and cancel noise from other directions. Furthermore, the audio processing unit can prioritize processing sound from a specific direction (e.g., sound from the front) and cancel noise from other directions. Additionally, the audio processing unit can prioritize processing sound from a direction specified by the user and cancel noise from other directions. This allows the audio processing unit to detect the direction of the sound and prioritize processing sounds coming from a specific direction.

[0040] The audio processing unit can select an appropriate noise cancellation method based on ambient temperature and humidity during noise cancellation. For example, the audio processing unit can acquire ambient temperature and humidity using temperature sensors and humidity sensors. For instance, if the ambient temperature is high, the audio processing unit can prioritize canceling specific noise patterns. For example, if the ambient temperature is high, the audio processing unit can prioritize canceling specific noise patterns such as traffic noise and wind noise. Furthermore, if the ambient humidity is high, the audio processing unit can prioritize canceling specific noise patterns. For example, if the ambient humidity is high, the audio processing unit can prioritize canceling specific noise patterns such as human speech and machine sounds. In addition, if the ambient temperature and humidity fluctuate, the audio processing unit can analyze environmental information in real time and dynamically select the optimal cancellation method. This allows the audio processing unit to select the optimal noise cancellation method by taking into account the ambient temperature and humidity.

[0041] During noise cancellation, the audio processing unit can select an optimal method by referring to the user's past noise canceling history. The audio processing unit, for example, acquires and analyzes the user's past noise canceling history. The noise canceling history includes, for example, past setting data and user feedback. For example, the audio processing unit can select an optimal method based on noise canceling settings used by the user in the past. For example, the audio processing unit can select an optimal method for a specific noise pattern based on noise canceling settings used by the user in the past. Furthermore, the audio processing unit can analyze the user's past noise canceling history and select an optimal method for a specific noise pattern. For example, the audio processing unit can analyze the user's past noise canceling history and select an optimal method for a specific noise pattern. Furthermore, the audio processing unit can select a method suitable for a specific environment from the user's past noise canceling history. For example, the audio processing unit can select a method suitable for a specific environment from the user's past noise canceling history. In this way, the audio processing unit can select an optimal noise canceling method by referring to the user's past noise canceling history.

[0042] The translation department can prioritize the translation of specific technical terms and slang during the translation process. For example, the department can prioritize the translation of terms and slang related to a particular field of expertise. Technical terms and slang include, for example, medical terms, technical terms, and regional slang. For example, the department can prioritize the translation of terms related to a particular field of expertise. For example, the department can prioritize the translation of terms related to the medical field. The department can also prioritize the translation of slang used by users. For example, the department can prioritize the translation of slang used in a particular region or community. Furthermore, the department can prioritize the translation of technical terms used in a particular industry or community. For example, the department can prioritize the translation of terms related to the technology field. This allows the department to provide more appropriate translations by prioritizing the translation of specific technical terms and slang.

[0043] The translation unit can analyze the context of the audio during translation to generate a more natural translation. For example, the translation unit can analyze the context of the audio and generate an appropriate translation. The context of the audio includes, for example, the surrounding conversation and related topics. Furthermore, the translation unit can generate a translation using appropriate vocabulary and grammar based on the context. For example, the translation unit can analyze related topics and generate a translation using appropriate vocabulary and grammar. As a result, the translation unit generates a more natural translation by analyzing the context of the audio.

[0044] The translation unit can select the optimal translation method by referring to the user's past translation history during translation. For example, the translation unit can retrieve and analyze the user's past translation history. This history includes, for example, past translation data and user feedback. For instance, the translation unit can select the optimal method based on the translation methods the user has used in the past. For example, the translation unit can prioritize the use of specific expressions based on the translation methods the user has used in the past. Furthermore, the translation unit can analyze the user's past translation history and select a method appropriate for a specific context. This allows the translation unit to select the optimal translation method by referring to the user's past translation history.

[0045] The translation unit can adjust the difficulty of the translation according to the user's language level. For example, the translation unit can evaluate the user's language level and adjust the difficulty of the translation accordingly. Language levels include, for example, beginner, intermediate, and advanced. For example, if the user is a beginner, the translation unit can provide a simple and easy-to-understand translation. For example, if the user is a beginner, the translation unit can provide a translation using simple and easy-to-understand expressions. For example, if the user is an intermediate, the translation unit can provide a translation of appropriate difficulty. For example, if the user is an intermediate, the translation unit can provide a translation using expressions of appropriate difficulty. Furthermore, if the user is an advanced, the translation unit can provide a detailed and advanced translation. For example, if the user is an advanced, the translation unit can provide a translation using detailed and advanced expressions. In this way, the translation unit can provide more appropriate translations by adjusting the difficulty of the translation according to the user's language level.

[0046] The audio output unit can emphasize and output specific frequency bands when outputting audio. For example, the audio output unit can emphasize and output frequency bands that mainly contain human voices. Human voices typically fall within the frequency band of 300Hz to 3400Hz. For example, the audio output unit can emphasize and output this frequency band to make it easier to hear. The audio output unit can also emphasize and output frequency bands based on the phonetic characteristics of a particular language. For example, the audio output unit can emphasize and output specific frequency bands based on the phonetic characteristics of a particular language. Furthermore, the audio output unit can emphasize and output specific sounds specified by the user (for example, alarm sounds). For example, the audio output unit can emphasize and output specific sounds specified by the user. In this way, the audio output unit makes the audio easier to hear by emphasizing specific frequency bands.

[0047] The audio output unit can select the optimal audio output method based on the user's auditory characteristics when outputting audio. For example, the audio output unit can evaluate the user's auditory characteristics and select the optimal audio output method based on them. Auditory characteristics include, for example, the results of hearing tests and user feedback. For example, the audio output unit can emphasize and output a specific frequency band based on the user's auditory characteristics. For example, the audio output unit can emphasize and output a specific frequency band based on the user's auditory characteristics. The audio output unit can also appropriately adjust the volume based on the user's auditory characteristics. For example, the audio output unit can appropriately adjust the volume based on the user's auditory characteristics. Furthermore, the audio output unit can perform filtering to improve the clarity of the audio based on the user's auditory characteristics. For example, the audio output unit can perform filtering to improve the clarity of the audio based on the user's auditory characteristics. As a result, the audio output unit improves the clarity of the audio by selecting the optimal audio output method based on the user's auditory characteristics.

[0048] The audio output unit can select an optimal audio output method by taking into account device information of the user when outputting audio. The audio output unit, for example, acquires device information of the user and selects an optimal audio output method based on the acquired device information. The device information includes, for example, the type of device and device setting information. For example, when the user is using a smartphone, the audio output unit can adjust the audio output based on speaker characteristics of the device. For example, when the user is using a smartphone, the audio output unit can adjust the audio output based on speaker characteristics of the device. Furthermore, when the user is using headphones, the audio output unit can adjust the audio output based on headphone characteristics. For example, when the user is using headphones, the audio output unit can adjust the audio output based on headphone characteristics. Furthermore, when the user is using an in-car system, the audio output unit can adjust the audio output based on acoustic characteristics of the car interior. For example, when the user is using an in-car system, the audio output unit can adjust the audio output based on acoustic characteristics of the car interior. In this way, the audio output unit can select an optimal audio output method by taking into account the device information of the user.

[0049] When outputting audio, the audio output unit can select the optimal method by referring to the user's past audio output history. The audio output unit, for example, acquires and analyzes the user's past audio output history. The audio output history includes, for example, past setting data and user feedback. For example, the audio output unit can select the optimal method based on audio output settings used by the user in the past. For example, the audio output unit can preferentially use a specific audio output method based on the audio output settings used by the user in the past. Furthermore, the audio output unit can analyze the user's past audio output history and select a method suitable for a specific environment. For example, the audio output unit can analyze the user's past audio output history and select a method suitable for a specific environment. In this way, the audio output unit can select the optimal audio output method by referring to the user's past audio output history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The speech translation system may also include a history reference unit that refers to the user's past translation history and selects the most suitable translation method. For example, the history reference unit can prioritize the use of specific expressions based on translation methods previously used by the user. Furthermore, the history reference unit can analyze the user's past translation history and select a method appropriate for a particular context. Additionally, the history reference unit can select a method appropriate for a particular environment from the user's past translation history. As a result, the speech translation system can provide more appropriate translations by referring to the user's past translation history.

[0052] The voice translation system may further include a location information analysis unit that prioritizes the collection of relevant audio based on the user's geographical location. For example, if the user is in a tourist destination, the location information analysis unit can prioritize the collection of audio related to that tourist destination. If the user is on the move, it can prioritize the collection of highly relevant audio considering the geographical location of the destination. Furthermore, if the user is participating in a specific event, it can prioritize the collection of audio related to that event. In this way, the voice translation system can prioritize the collection of highly relevant audio by considering the user's geographical location.

[0053] The voice translation system may also include a social media analysis unit that analyzes the user's social media activity and collects relevant audio. For example, if the user posts about a specific topic, the social media analysis unit can prioritize collecting audio related to that topic. It can also analyze the content of posts from accounts the user follows and prioritize collecting audio related to that content. Furthermore, it can prioritize collecting audio related to events the user participates in on social media. This allows the voice translation system to effectively collect relevant audio by analyzing the user's social media activity.

[0054] The voice translation system may further include an auditory characteristics analysis unit that selects the optimal voice output method based on the user's auditory characteristics. For example, the auditory characteristics analysis unit can emphasize and output specific frequency bands based on the user's auditory characteristics. It can also appropriately adjust the volume based on the user's auditory characteristics. Furthermore, the auditory characteristics analysis unit can perform filtering to improve the clarity of the voice based on the user's auditory characteristics. As a result, the voice translation system improves voice clarity by selecting the optimal voice output method based on the user's auditory characteristics.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The collection unit collects external sounds. The collection unit can collect external sounds such as ambient sounds, conversations, and music. The collection unit can collect external sounds using, for example, a high-sensitivity microphone. Step 2: The audio processing unit performs noise cancellation on the audio collected by the acquisition unit. The audio processing unit effectively cancels external noise using, for example, active noise cancellation technology. Active noise cancellation technology can be implemented using methods such as feedforward, feedback, or hybrid methods. For example, in the feedforward method, external noise is collected by a microphone and canceled out with sound waves of the opposite phase. In the feedback method, noise inside the ear is collected by a microphone and canceled out with sound waves of the opposite phase. In the hybrid method, both the feedforward and feedback methods are combined to cancel out the noise. Step 3: The translation unit translates the audio after the noise has been canceled out by the audio processing unit. The translation unit translates the audio instantly, for example, using a real-time translation engine. The translation engine could be, for example, Google Translate, Microsoft Translator, or a proprietary engine. Step 4: The audio output unit provides the audio translated by the translation unit. The audio output unit provides the translated audio to the user, for example, using a speaker, earphones, or headset.

[0057] (Example 2) An embodiment of the present invention provides a voice translation system that cancels external noise using active noise cancellation technology and immediately provides the user with translated audio. This voice translation system collects external noise with a microphone and processes the collected audio using active noise cancellation technology to cancel noise. The noise-free audio is then input to a translation engine to immediately generate translated audio. Finally, the generated translated audio is provided to the user. For example, the voice translation system significantly reduces communication barriers in international environments and among people with diverse linguistic backgrounds, enabling more natural and smoother conversations. For instance, the voice translation system is envisioned for use in situations such as conferences, business meetings, and travel. By translating external audio in real time and providing it to the user, the voice translation system facilitates communication between people who speak different languages. For example, the voice translation system collects external noise with a high-precision microphone and removes noise using active noise cancellation technology. Next, the noise-free audio is input to a high-performance translation engine to immediately generate translated audio. Finally, the generated translated audio is provided to the user with clear sound quality. This allows voice translation systems to provide users with an experience as if the other person were speaking in their own language. As a result, voice translation systems can significantly reduce communication barriers in international settings and between people with diverse linguistic backgrounds, enabling more natural and smoother conversations.

[0058] A speech translation system according to an embodiment includes a collection unit, a speech processing unit, a translation unit, and a speech output unit. The collection unit collects external speech. The collection unit can collect external speech, such as environmental sounds, conversations, and music. The collection unit collects external speech using, for example, a high-sensitivity microphone. The speech processing unit performs noise cancellation on the speech collected by the collection unit. The speech processing unit effectively cancels external noise using, for example, active noise canceling technology. Active noise canceling technology can be realized using methods such as a feedforward method, a feedback method, or a hybrid method. For example, the feedforward method collects external noise using a microphone and cancels the noise with antiphase sound waves. The feedback method collects noise inside the ear using a microphone and cancels the noise with antiphase sound waves. The hybrid method combines both the feedforward method and the feedback method to cancel noise. The translation unit translates the speech from which noise has been canceled by the speech processing unit. The translation unit instantly translates the speech using, for example, a real-time translation engine. The translation engine can be, for example, Google Translate, Microsoft Translator, or a proprietary engine. The audio output unit provides the audio translated by the translation unit. The audio output unit provides the translated audio to the user using, for example, a speaker, earphones, or headset. As a result, the audio translation system according to this embodiment can provide the user with a natural and smooth conversational experience by collecting external audio, noise canceling it, translating it, and providing it.

[0059] The sound collection unit is equipped with microphones. These microphones may include, for example, condenser microphones, dynamic microphones, or directional microphones. Condenser microphones offer high sensitivity and cover a wide frequency range, enabling high-precision collection of ambient sounds and conversations. Dynamic microphones are highly durable and can capture clear audio even in noisy environments. Directional microphones prioritize sound collection from specific directions, effectively eliminating ambient noise. For example, the sound collection unit can accurately capture conversations in a conference room using a condenser microphone. It can also capture clear audio in noisy outdoor environments using a dynamic microphone. Furthermore, the sound collection unit can prioritize sound collection from specific directions using a directional microphone, effectively eliminating ambient noise. This allows the sound collection unit to effectively capture external sounds.

[0060] The audio processing unit incorporates active noise cancellation technology. The audio processing unit incorporates active noise cancellation technologies such as feedforward, feedback, and hybrid methods. The feedforward method collects external noise with a microphone and cancels it with sound waves of the opposite phase. For example, the audio processing unit can effectively cancel external noise using the feedforward method. The feedback method collects noise from inside the ear with a microphone and cancels it with sound waves of the opposite phase. For example, the audio processing unit can effectively cancel noise from inside the ear using the feedback method. The hybrid method combines both the feedforward and feedback methods to cancel noise. For example, the audio processing unit can effectively cancel both external noise and noise from inside the ear using the hybrid method. This allows the audio processing unit to effectively cancel external noise.

[0061] The translation unit includes a translation engine. The translation unit includes a translation engine such as Google Translate, Microsoft Translator, or a proprietary engine. Google Translate supports multiple languages ​​and can provide highly accurate translations. Microsoft Translator has a real-time translation function and can provide instantly translated audio. The proprietary engine can provide a translation function customized for specific applications and needs. For example, the translation unit can use Google Translate to provide highly accurate translations in multiple languages. The translation unit can also use Microsoft Translator to provide instantly translated audio. The translation unit can also use a proprietary engine to provide a customized translation function tailored for specific applications and needs. This allows the translation unit to instantly translate collected audio.

[0062] The audio output unit is equipped with a speaker. The audio output unit may include a built-in speaker, an external speaker, or a Bluetooth speaker. A built-in speaker is integrated into the device, allowing for easy audio output. An external speaker can output high-quality, high-volume audio. A Bluetooth speaker can connect wirelessly and be freely positioned. For example, the audio output unit can easily output audio using its built-in speaker. Furthermore, the audio output unit can output high-quality, high-volume audio using an external speaker. Additionally, the audio output unit can connect wirelessly using a Bluetooth speaker and output audio in a freely positioned location. This allows the audio output unit to provide translated audio to the user.

[0063] The data collection unit can estimate the user's emotions and filter the collected audio based on the estimated emotions. The data collection unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and biometric data. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. For example, the data collection unit can use voice analysis to analyze the tone and speed of the user's voice and estimate whether they are tense or relaxed. Facial expression analysis is a method of estimating emotions by capturing the user's facial expressions with a camera. For example, the data collection unit can use facial expression analysis to capture the user's facial expressions with a camera and estimate whether they are happy or sad. Biometric data is a method of estimating emotions by collecting the user's biometric data (such as heart rate and skin electrical activity) with sensors. For example, the data collection unit can use biometric data to analyze the user's heart rate and skin electrical activity and estimate whether they are excited or calm. The data collection unit filters the collected audio based on the estimated emotions of the user. For example, if the user is tense, the sound collection unit will more strongly filter out ambient noise to help them relax. Conversely, if the user is relaxed, the unit can collect only the necessary sounds while retaining natural ambient sounds. Furthermore, if the user is focused, the unit can prioritize collecting sounds within a specific frequency range and filter out other sounds. In this way, the sound collection unit can collect more appropriate audio by filtering sounds according to the user's emotions.

[0064] The collection unit can analyze surrounding environmental sounds during collection and select an optimal collection method. For example, the collection unit analyzes surrounding environmental sounds in real time and selects an optimal collection method. Surrounding environmental sounds include, for example, traffic sounds, wind sounds, and people talking. For example, when the surroundings are noisy, the collection unit can use a directional microphone to collect sounds from a specific direction. A directional microphone can preferentially collect sounds from a specific direction and effectively remove ambient noise. For example, the collection unit can use a directional microphone to collect the voice of a specific speaker in a conference room. Furthermore, when the surroundings are quiet, the collection unit can use an omnidirectional microphone to collect sounds from a wide range. An omnidirectional microphone can evenly collect sounds from all directions. For example, the collection unit can use an omnidirectional microphone to collect outdoor natural sounds. Furthermore, when the surrounding environmental sounds fluctuate, the collection unit can analyze the environmental sounds in real time and dynamically select an optimal collection method. For example, the collection unit can switch between a directional microphone and an omnidirectional microphone depending on the fluctuations in the environmental sounds. This allows the data collection unit to select the optimal collection method by analyzing the surrounding ambient sounds.

[0065] During collection, the collection unit can prioritize collection of sounds in a specific frequency band. For example, the collection unit prioritizes collection of a frequency band that mainly contains human voices and filters out other sounds. Human voices are typically included in a frequency band from 300 Hz to 3400 Hz. For example, the collection unit can prioritize collection of this frequency band and filter out sounds in other frequency bands. The collection unit can also prioritize collection of a corresponding frequency band based on the sound characteristics of a specific language. For example, the collection unit can prioritize collection of a specific frequency band based on the sound characteristics of a specific language and filter out other sounds. Furthermore, the collection unit can prioritize collection of a specific sound (e.g., an alarm sound) specified by the user. For example, the collection unit can prioritize collection of a specific sound specified by the user and filter out other sounds. In this way, the collection unit can effectively collect required sounds by prioritize collection of sounds in a specific frequency band.

[0066] The collection unit can estimate the user's emotion and determine the priority of the audio to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, biometric data, etc. Voice analysis is a method of analyzing the tone and speed of the user's voice to estimate emotion. For example, the collection unit can use voice analysis to analyze the tone and speed of the user's voice to estimate whether the user is nervous or relaxed. Facial expression analysis is a method of capturing the user's facial expression with a camera to estimate emotion. For example, the collection unit can use facial expression analysis to capture the user's facial expression with a camera to estimate whether the user is happy or sad. Biometric data is a method of collecting the user's biological data (such as heart rate and electrodermal activity) with a sensor to estimate emotion. For example, the collection unit can use biometric data to analyze the user's heart rate and electrodermal activity to estimate whether the user is excited or calm. The collection unit determines the priority of the audio to be collected based on the estimated user's emotion. For example, if the user is nervous, the collection unit can prioritize collecting important sounds and filter out other sounds. Also, if the user is relaxed, the collection unit can prioritize collecting sounds including natural environmental sounds. Furthermore, if the user is concentrating, the collection unit can prioritize collecting sounds in a specific frequency band and filter out other sounds. This allows the collection unit to prioritize collecting important sounds by determining the priority of sounds according to the user's emotions.

[0067] The data collection unit can prioritize collecting relevant audio based on the user's geographical location information during data collection. The data collection unit obtains the user's geographical location information using, for example, GPS data and Wi-Fi location information. GPS data can pinpoint the user's current location with high accuracy. Wi-Fi location information can pinpoint the user's location based on the location information of Wi-Fi access points. For example, if the user is in a specific location, the data collection unit can prioritize collecting audio related to that location. For example, if the user is in a tourist destination, the data collection unit can prioritize collecting audio related to that tourist destination. Furthermore, if the user is on the move, the data collection unit can prioritize collecting highly relevant audio by considering the geographical location information of the destination. For example, if the user is in a moving vehicle, the data collection unit can prioritize collecting relevant audio based on the geographical location information of the destination. In addition, if the user is participating in a specific event, the data collection unit can prioritize collecting audio related to that event. For example, if the user is attending a concert, the data collection unit can prioritize collecting audio related to that concert. In this way, the data collection unit can prioritize collecting highly relevant audio by considering the user's geographical location information.

[0068] The data collection unit can analyze a user's social media activity and collect relevant audio during the collection process. For example, the data collection unit can analyze the user's social media posts and followers. Social media activity analysis includes, for example, prioritizing the collection of audio related to a specific topic if the user posts about that topic. The data collection unit can also analyze the posts of accounts the user follows on social media and collect relevant audio. For example, the data collection unit can analyze the posts of accounts the user follows and prioritize the collection of audio related to that content. Furthermore, the data collection unit can prioritize the collection of audio related to events the user is participating in on social media. For example, the data collection unit can prioritize the collection of audio related to events the user is participating in on social media. In this way, the data collection unit can effectively collect relevant audio by analyzing the user's social media activity.

[0069] The audio processing unit can estimate the user's emotions and adjust the intensity of noise canceling based on the estimated user's emotions. The audio processing unit estimates the user's emotions using, for example, audio analysis, facial expression analysis, biometric data, etc. Audio analysis is a method of estimating emotions by analyzing the tone and speed of a user's voice. For example, the audio processing unit can use audio analysis to analyze the tone and speed of a user's voice and estimate whether the user is nervous or relaxed. Facial expression analysis is a method of estimating emotions by capturing the user's facial expression with a camera. For example, the audio processing unit can use facial expression analysis to capture the user's facial expression with a camera and estimate whether the user is happy or sad. Biometric data is a method of estimating emotions by collecting the user's biological data (such as heart rate and electrodermal activity) with a sensor. For example, the audio processing unit can use biometric data to analyze the user's heart rate and electrodermal activity and estimate whether the user is excited or calm. The audio processing unit adjusts the intensity of noise canceling based on the estimated user's emotions. For example, if the user is nervous, the audio processing unit increases the noise canceling intensity to more effectively remove ambient noise. Alternatively, if the user is relaxed, the audio processing unit moderately adjusts the noise canceling intensity to preserve natural ambient sounds. Furthermore, if the user is concentrating, the audio processing unit can preferentially cancel noise in specific frequency bands. This allows the audio processing unit to adjust the noise canceling intensity according to the user's emotions, enabling more effective noise cancellation.

[0070] The audio processing unit can learn specific noise patterns during noise cancellation to improve the accuracy of noise cancellation. For example, the audio processing unit can learn surrounding environmental sounds in real time and identify specific noise patterns to improve the accuracy of cancellation. Specific noise patterns include, for example, traffic noise, wind noise, and people talking. For example, the audio processing unit can learn surrounding environmental sounds in real time and identify specific noise patterns to improve the accuracy of cancellation. The audio processing unit can also analyze a user's past noise cancellation history and learn specific noise patterns to improve the accuracy of cancellation. For example, the audio processing unit can analyze a user's past noise cancellation history and learn specific noise patterns to improve the accuracy of cancellation. Furthermore, the audio processing unit can learn noise patterns in a specific location (e.g., an office or a cafe) and perform noise cancellation appropriate for that location. For example, the audio processing unit can learn noise patterns in a specific location and perform noise cancellation appropriate for that location. In this way, the audio processing unit improves the accuracy of noise cancellation by learning specific noise patterns.

[0071] The audio processing unit can detect the direction of sound during noise cancellation and prioritize processing of sound from a specific direction. The audio processing unit can detect the direction of sound using, for example, a microphone array or sound source localization technology. A microphone array is a technology that uses multiple microphones to detect the direction of sound arrival. For example, the audio processing unit can use a microphone array to detect the direction of sound and prioritize processing of sound from a specific direction. Sound source localization technology is a technology that detects the direction of sound using differences in arrival time or intensity. For example, the audio processing unit can use sound source localization technology to detect the direction of sound and prioritize processing of sound from a specific direction. For example, the audio processing unit can prioritize processing sound from the direction the user is facing and cancel noise from other directions. Furthermore, the audio processing unit can prioritize processing sound from a specific direction (e.g., sound from the front) and cancel noise from other directions. Additionally, the audio processing unit can prioritize processing sound from a direction specified by the user and cancel noise from other directions. This allows the audio processing unit to detect the direction of the sound and prioritize processing sounds coming from a specific direction.

[0072] The audio processing unit can estimate the user's emotions and adjust the timing of noise cancellation based on the estimated emotions. The audio processing unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and biometric data. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. For example, the audio processing unit can use voice analysis to analyze the tone and speed of the user's voice and estimate whether they are tense or relaxed. Facial expression analysis is a method of estimating emotions by capturing the user's facial expressions with a camera. For example, the audio processing unit can use facial expression analysis to capture the user's facial expressions with a camera and estimate whether they are happy or sad. Biometric data is a method of estimating emotions by collecting the user's biometric data (such as heart rate and skin electrical activity) with sensors. For example, the audio processing unit can use biometric data to analyze the user's heart rate and skin electrical activity and estimate whether they are excited or calm. The audio processing unit adjusts the timing of noise cancellation based on the estimated emotions of the user. For example, if the user is stressed, the audio processing unit quickly adjusts the timing of noise cancellation to instantly eliminate ambient noise. If the user is relaxed, the audio processing unit adjusts the timing of noise cancellation appropriately, allowing natural ambient sounds to be preserved. Furthermore, if the user is focused, the audio processing unit can adjust the timing to prioritize the cancellation of noise in specific frequency bands. This allows the audio processing unit to provide more effective noise cancellation by adjusting the timing of noise cancellation according to the user's emotions.

[0073] The audio processing unit can select an appropriate noise cancellation method based on ambient temperature and humidity during noise cancellation. For example, the audio processing unit can acquire ambient temperature and humidity using temperature sensors and humidity sensors. For instance, if the ambient temperature is high, the audio processing unit can prioritize canceling specific noise patterns. For example, if the ambient temperature is high, the audio processing unit can prioritize canceling specific noise patterns such as traffic noise and wind noise. Furthermore, if the ambient humidity is high, the audio processing unit can prioritize canceling specific noise patterns. For example, if the ambient humidity is high, the audio processing unit can prioritize canceling specific noise patterns such as human speech and machine sounds. In addition, if the ambient temperature and humidity fluctuate, the audio processing unit can analyze environmental information in real time and dynamically select the optimal cancellation method. This allows the audio processing unit to select the optimal noise cancellation method by taking into account the ambient temperature and humidity.

[0074] During noise cancellation, the audio processing unit can select an optimal method by referring to the user's past noise canceling history. The audio processing unit, for example, acquires and analyzes the user's past noise canceling history. The noise canceling history includes, for example, past setting data and user feedback. For example, the audio processing unit can select an optimal method based on noise canceling settings used by the user in the past. For example, the audio processing unit can select an optimal method for a specific noise pattern based on noise canceling settings used by the user in the past. Furthermore, the audio processing unit can analyze the user's past noise canceling history and select an optimal method for a specific noise pattern. For example, the audio processing unit can analyze the user's past noise canceling history and select an optimal method for a specific noise pattern. Furthermore, the audio processing unit can select a method suitable for a specific environment from the user's past noise canceling history. For example, the audio processing unit can select a method suitable for a specific environment from the user's past noise canceling history. In this way, the audio processing unit can select an optimal noise canceling method by referring to the user's past noise canceling history.

[0075] The translation unit can estimate the user's emotions and adjust the translation's expression based on those estimated emotions. For example, the translation unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and biometric data. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. For example, the translation unit can use voice analysis to analyze the tone and speed of the user's voice and estimate whether they are tense or relaxed. Facial expression analysis is a method of estimating emotions by capturing the user's facial expressions with a camera. For example, the translation unit can use facial expression analysis to capture the user's facial expressions with a camera and estimate whether they are happy or sad. Biometric data is a method of estimating emotions by collecting the user's biometric data (such as heart rate and skin electrical activity) with sensors. For example, the translation unit can use biometric data to analyze the user's heart rate and skin electrical activity and estimate whether they are excited or calm. The translation unit adjusts the translation's expression based on the estimated user's emotions. For example, if the user is tense, the translation unit uses a simple and easy-to-understand expression. Furthermore, when the user is relaxed, the translator can use detailed and polite language. Conversely, when the user is excited, the translator can use emotionally emphasizing language. This allows the translator to provide more appropriate translations by adjusting the language used according to the user's emotions.

[0076] The translation department can prioritize the translation of specific technical terms and slang during the translation process. For example, the department can prioritize the translation of terms and slang related to a particular field of expertise. Technical terms and slang include, for example, medical terms, technical terms, and regional slang. For example, the department can prioritize the translation of terms related to a particular field of expertise. For example, the department can prioritize the translation of terms related to the medical field. The department can also prioritize the translation of slang used by users. For example, the department can prioritize the translation of slang used in a particular region or community. Furthermore, the department can prioritize the translation of technical terms used in a particular industry or community. For example, the department can prioritize the translation of terms related to the technology field. This allows the department to provide more appropriate translations by prioritizing the translation of specific technical terms and slang.

[0077] The translation unit can analyze the context of the audio during translation to generate a more natural translation. For example, the translation unit can analyze the context of the audio and generate an appropriate translation. The context of the audio includes, for example, the surrounding conversation and related topics. Furthermore, the translation unit can generate a translation using appropriate vocabulary and grammar based on the context. For example, the translation unit can analyze related topics and generate a translation using appropriate vocabulary and grammar. As a result, the translation unit generates a more natural translation by analyzing the context of the audio.

[0078] The translation unit can estimate the user's emotions and adjust the translation speed based on the estimated user's emotions. The translation unit estimates the user's emotions using, for example, voice analysis, facial expression analysis, biometric data, etc. Voice analysis is a method of estimating emotions by analyzing the tone and speed of a user's voice. For example, the translation unit can use voice analysis to analyze the tone and speed of a user's voice to estimate whether the user is nervous or relaxed. Facial expression analysis is a method of estimating emotions by capturing the user's facial expressions with a camera. For example, the translation unit can use facial expression analysis to capture the user's facial expressions with a camera to estimate whether the user is happy or sad. Biometric data is a method of estimating emotions by collecting the user's biological data (such as heart rate and electrodermal activity) with a sensor. For example, the translation unit can use biometric data to analyze the user's heart rate and electrodermal activity to estimate whether the user is excited or calm. The translation unit adjusts the translation speed based on the estimated user's emotions. For example, if the user is in a hurry, the translation unit increases the translation speed. Furthermore, when the user is relaxed, the translation unit can adjust the translation speed appropriately. Furthermore, when the user is concentrating, the translation unit can adjust the translation speed appropriately. This allows the translation unit to adjust the translation speed according to the user's emotions, thereby providing a more appropriate translation.

[0079] The translation unit can select the optimal translation method by referring to the user's past translation history during translation. For example, the translation unit can retrieve and analyze the user's past translation history. This history includes, for example, past translation data and user feedback. For instance, the translation unit can select the optimal method based on the translation methods the user has used in the past. For example, the translation unit can prioritize the use of specific expressions based on the translation methods the user has used in the past. Furthermore, the translation unit can analyze the user's past translation history and select a method appropriate for a specific context. This allows the translation unit to select the optimal translation method by referring to the user's past translation history.

[0080] The translation unit can adjust the difficulty of the translation according to the user's language level. For example, the translation unit can evaluate the user's language level and adjust the difficulty of the translation accordingly. Language levels include, for example, beginner, intermediate, and advanced. For example, if the user is a beginner, the translation unit can provide a simple and easy-to-understand translation. For example, if the user is a beginner, the translation unit can provide a translation using simple and easy-to-understand expressions. For example, if the user is an intermediate, the translation unit can provide a translation of appropriate difficulty. For example, if the user is an intermediate, the translation unit can provide a translation using expressions of appropriate difficulty. Furthermore, if the user is an advanced, the translation unit can provide a detailed and advanced translation. For example, if the user is an advanced, the translation unit can provide a translation using detailed and advanced expressions. In this way, the translation unit can provide more appropriate translations by adjusting the difficulty of the translation according to the user's language level.

[0081] The audio output unit can estimate the user's emotions and adjust the volume of the audio output based on the estimated emotions. The audio output unit estimates the user's emotions using methods such as voice analysis, facial expression analysis, and biometric data. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. For example, the audio output unit can use voice analysis to analyze the tone and speed of the user's voice and estimate whether they are tense or relaxed. Facial expression analysis is a method of estimating emotions by capturing the user's facial expression with a camera. For example, the audio output unit can use facial expression analysis to capture the user's facial expression with a camera and estimate whether they are happy or sad. Biometric data is a method of estimating emotions by collecting the user's biometric data (such as heart rate and skin electrical activity) with sensors. For example, the audio output unit can use biometric data to analyze the user's heart rate and skin electrical activity and estimate whether they are excited or calm. The audio output unit adjusts the volume of the audio output based on the estimated emotions of the user. For example, if the user is nervous, the audio output unit will set the volume lower to create a calm atmosphere. If the user is relaxed, the audio output unit will adjust the volume appropriately to produce a natural sound. Furthermore, if the user is excited, the audio output unit will set the volume higher to create an energetic atmosphere. In this way, the audio output unit can provide more appropriate audio output by adjusting the volume according to the user's emotions.

[0082] The audio output unit can emphasize and output specific frequency bands when outputting audio. For example, the audio output unit can emphasize and output frequency bands that mainly contain human voices. Human voices typically fall within the frequency band of 300Hz to 3400Hz. For example, the audio output unit can emphasize and output this frequency band to make it easier to hear. The audio output unit can also emphasize and output frequency bands based on the phonetic characteristics of a particular language. For example, the audio output unit can emphasize and output specific frequency bands based on the phonetic characteristics of a particular language. Furthermore, the audio output unit can emphasize and output specific sounds specified by the user (for example, alarm sounds). For example, the audio output unit can emphasize and output specific sounds specified by the user. In this way, the audio output unit makes the audio easier to hear by emphasizing specific frequency bands.

[0083] The audio output unit can select the optimal audio output method based on the user's auditory characteristics when outputting audio. For example, the audio output unit can evaluate the user's auditory characteristics and select the optimal audio output method based on them. Auditory characteristics include, for example, the results of hearing tests and user feedback. For example, the audio output unit can emphasize and output a specific frequency band based on the user's auditory characteristics. For example, the audio output unit can emphasize and output a specific frequency band based on the user's auditory characteristics. The audio output unit can also appropriately adjust the volume based on the user's auditory characteristics. For example, the audio output unit can appropriately adjust the volume based on the user's auditory characteristics. Furthermore, the audio output unit can perform filtering to improve the clarity of the audio based on the user's auditory characteristics. For example, the audio output unit can perform filtering to improve the clarity of the audio based on the user's auditory characteristics. As a result, the audio output unit improves the clarity of the audio by selecting the optimal audio output method based on the user's auditory characteristics.

[0084] The audio output unit can estimate the user's emotion and adjust the timing of audio output based on the estimated user's emotion. The audio output unit estimates the user's emotion using, for example, voice analysis, facial expression analysis, biometric data, etc. Voice analysis is a method of estimating emotion by analyzing the tone and speed of the user's voice. For example, the audio output unit can use voice analysis to analyze the tone and speed of the user's voice and estimate whether the user is nervous or relaxed. Facial expression analysis is a method of estimating emotion by capturing the user's facial expression with a camera. For example, the audio output unit can use facial expression analysis to capture the user's facial expression with a camera and estimate whether the user is happy or sad. Biometric data is a method of estimating emotion by collecting the user's biological data (such as heart rate and electrodermal activity) with a sensor. For example, the audio output unit can use biometric data to analyze the user's heart rate and electrodermal activity and estimate whether the user is excited or calm. The audio output unit adjusts the timing of audio output based on the estimated user's emotion. For example, if the user is nervous, the audio output unit quickly adjusts the timing of the audio output to provide information immediately. Also, if the user is relaxed, the audio output unit can appropriately adjust the timing of the audio output to provide information at natural intervals. Furthermore, if the user is concentrating, the audio output unit can output audio at specific timing to effectively convey information. Thus, the audio output unit can provide audio at more appropriate timing by adjusting the timing of the audio output according to the user's emotions.

[0085] The audio output unit can select an optimal audio output method by taking into account device information of the user when outputting audio. The audio output unit, for example, acquires device information of the user and selects an optimal audio output method based on the acquired device information. The device information includes, for example, the type of device and device setting information. For example, when the user is using a smartphone, the audio output unit can adjust the audio output based on speaker characteristics of the device. For example, when the user is using a smartphone, the audio output unit can adjust the audio output based on speaker characteristics of the device. Furthermore, when the user is using headphones, the audio output unit can adjust the audio output based on headphone characteristics. For example, when the user is using headphones, the audio output unit can adjust the audio output based on headphone characteristics. Furthermore, when the user is using an in-car system, the audio output unit can adjust the audio output based on acoustic characteristics of the car interior. For example, when the user is using an in-car system, the audio output unit can adjust the audio output based on acoustic characteristics of the car interior. In this way, the audio output unit can select an optimal audio output method by taking into account the device information of the user.

[0086] When outputting audio, the audio output unit can select the optimal method by referring to the user's past audio output history. The audio output unit, for example, acquires and analyzes the user's past audio output history. The audio output history includes, for example, past setting data and user feedback. For example, the audio output unit can select the optimal method based on audio output settings used by the user in the past. For example, the audio output unit can preferentially use a specific audio output method based on the audio output settings used by the user in the past. Furthermore, the audio output unit can analyze the user's past audio output history and select a method suitable for a specific environment. For example, the audio output unit can analyze the user's past audio output history and select a method suitable for a specific environment. In this way, the audio output unit can select the optimal audio output method by referring to the user's past audio output history. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, voice processing unit, translation unit, and voice output unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects external sound using the microphone 38B of the smart device 14. The voice processing unit cancels noise using active noise cancellation technology by the specific processing unit 290 of the data processing unit 12. The translation unit translates the speech using a translation engine by the specific processing unit 290 of the data processing unit 12. The voice output unit provides the translated speech to the user using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, audio processing unit, translation unit, and audio output unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects external sound using the microphone 238 of the smart glasses 214. The audio processing unit cancels noise using active noise cancellation technology by the specific processing unit 290 of the data processing unit 12. The translation unit translates the speech using a translation engine by the specific processing unit 290 of the data processing unit 12. The audio output unit provides the translated speech to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, audio processing unit, translation unit, and audio output unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects external audio using the microphone 238 of the headset terminal 314. The audio processing unit cancels noise using active noise cancellation technology by the specific processing unit 290 of the data processing unit 12. The translation unit translates the audio using a translation engine by the specific processing unit 290 of the data processing unit 12. The audio output unit provides the translated audio to the user using the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, voice processing unit, translation unit, and voice output unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit collects external voice using the microphone 238 of the robot 414. The voice processing unit cancels noise using active noise canceling technology by the specific processing unit 290 of the data processing device 12. The translation unit translates voice using a translation engine by the specific processing unit 290 of the data processing device 12. The voice output unit provides the translated voice to the user using the speaker 240 of the robot 414.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The speech translation system may further include an emotion analysis unit that analyzes the tone and speed of the user's voice and estimates the user's emotions. For example, if the user is nervous, the emotion analysis unit may provide the translated speech in a calmer tone. If the user is excited, the emotion analysis unit may provide the translated speech in an energetic tone. If the user is sad, the emotion analysis unit may provide the translated speech in a gentler tone. This allows the speech translation system to output speech in accordance with the user's emotions, thereby achieving more natural and empathetic communication.

[0089] The speech translation system may also include a history reference unit that refers to the user's past translation history and selects the most suitable translation method. For example, the history reference unit can prioritize the use of specific expressions based on translation methods previously used by the user. Furthermore, the history reference unit can analyze the user's past translation history and select a method appropriate for a particular context. Additionally, the history reference unit can select a method appropriate for a particular environment from the user's past translation history. As a result, the speech translation system can provide more appropriate translations by referring to the user's past translation history.

[0090] The voice translation system may further include a location information analysis unit that prioritizes the collection of relevant audio based on the user's geographical location. For example, if the user is in a tourist destination, the location information analysis unit can prioritize the collection of audio related to that tourist destination. If the user is on the move, it can prioritize the collection of highly relevant audio considering the geographical location of the destination. Furthermore, if the user is participating in a specific event, it can prioritize the collection of audio related to that event. In this way, the voice translation system can prioritize the collection of highly relevant audio by considering the user's geographical location.

[0091] The voice translation system may also include a social media analysis unit that analyzes the user's social media activity and collects relevant audio. For example, if the user posts about a specific topic, the social media analysis unit can prioritize collecting audio related to that topic. It can also analyze the content of posts from accounts the user follows and prioritize collecting audio related to that content. Furthermore, it can prioritize collecting audio related to events the user participates in on social media. This allows the voice translation system to effectively collect relevant audio by analyzing the user's social media activity.

[0092] The voice translation system may further include an auditory characteristics analysis unit that selects the optimal voice output method based on the user's auditory characteristics. For example, the auditory characteristics analysis unit can emphasize and output specific frequency bands based on the user's auditory characteristics. It can also appropriately adjust the volume based on the user's auditory characteristics. Furthermore, the auditory characteristics analysis unit can perform filtering to improve the clarity of the voice based on the user's auditory characteristics. As a result, the voice translation system improves voice clarity by selecting the optimal voice output method based on the user's auditory characteristics.

[0093] The speech translation system can further estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, the translation unit can use simple and easy-to-understand expressions. If the user is relaxed, the translation unit can use detailed and polite expressions. If the user is excited, the translation unit can use expressions that emphasize the emotions. This allows the translation unit to adjust the translation expression according to the user's emotions and provide a more appropriate translation.

[0094] The speech translation system can further estimate the user's emotions and adjust the noise canceling intensity based on the estimated user's emotions. For example, if the user is nervous, the speech processor can increase the noise canceling intensity to more effectively remove ambient noise. Alternatively, if the user is relaxed, the speech processor can moderately adjust the noise canceling intensity to preserve natural ambient sounds. Furthermore, if the user is concentrating, the speech processor can preferentially cancel noise in a specific frequency band. This allows the speech processor to adjust the noise canceling intensity according to the user's emotions, enabling more effective noise cancellation.

[0095] The speech translation system can further estimate the user's emotions and adjust the volume of the speech output based on the estimated user's emotions. For example, if the user is nervous, the speech output unit can set the speech output volume lower to create a calm atmosphere. If the user is relaxed, the speech output unit can adjust the speech output volume appropriately to output at a natural volume. If the user is excited, the speech output unit can set the speech output volume higher to create an energetic atmosphere. In this way, the speech output unit can provide more appropriate speech output by adjusting the speech output volume according to the user's emotions.

[0096] The speech translation system can further estimate the user's emotions and adjust the timing of speech output based on the estimated user emotions. For example, if the user is nervous, the speech output unit can quickly adjust the timing of speech output to provide information immediately. If the user is relaxed, the speech output unit can appropriately adjust the timing of speech output to provide information at natural intervals. Furthermore, if the user is concentrating, the speech output unit can output speech at a specific timing to effectively convey information. In this way, the speech output unit can provide speech at more appropriate timing by adjusting the timing of speech output according to the user's emotions.

[0097] The speech translation system can further estimate the user's emotions and determine the priority of the speech to be collected based on the estimated user's emotions. For example, if the user is nervous, the collection unit can prioritize collecting important speech and filter out other speech. Alternatively, if the user is relaxed, the collection unit can prioritize collecting speech including natural environmental sounds. Furthermore, if the user is concentrating, the collection unit can prioritize collecting speech in a specific frequency band and filter out other speech. In this way, the collection unit can prioritize collecting important speech by determining the priority of speech according to the user's emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The collection unit collects external sounds. The collection unit can collect external sounds such as ambient sounds, conversations, and music. The collection unit can collect external sounds using, for example, a high-sensitivity microphone. Step 2: The audio processing unit performs noise cancellation on the audio collected by the acquisition unit. The audio processing unit effectively cancels external noise using, for example, active noise cancellation technology. Active noise cancellation technology can be implemented using methods such as feedforward, feedback, or hybrid methods. For example, in the feedforward method, external noise is collected by a microphone and canceled out with sound waves of the opposite phase. In the feedback method, noise inside the ear is collected by a microphone and canceled out with sound waves of the opposite phase. In the hybrid method, both the feedforward and feedback methods are combined to cancel out the noise. Step 3: The translation unit translates the audio after the noise has been canceled out by the audio processing unit. The translation unit translates the audio instantly, for example, using a real-time translation engine. The translation engine could be, for example, Google Translate, Microsoft Translator, or a proprietary engine. Step 4: The audio output unit provides the audio translated by the translation unit. The audio output unit provides the translated audio to the user, for example, using a speaker, earphones, or headset.

[0100] 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.

[0101] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 collection unit that collects external sounds; a sound processing unit that performs noise cancellation on the sound collected by the collection unit; a translation unit that translates the voice from which noise has been canceled by the voice processing unit; an audio output unit that provides the audio translated by the translation unit; A system characterized by:

2. The collecting unit Equipped with a microphone 2. The system of claim 1.

3. The audio processing unit Equipped with active noise cancelling technology 2. The system of claim 1.

4. The translation unit Equipped with a translation engine 2. The system of claim 1.

5. The audio output unit Equipped with speakers 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and filter the collected audio based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting, analyze the surrounding environmental sounds and select the appropriate collection method.

2. The system of claim 1.

8. The collecting unit When collecting, prioritize collection of audio in a specific frequency band 2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and prioritize the audio to be collected based on the estimated user emotions.

2. The system of claim 1.

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