System

Abstract

An object of the system according to the embodiment is to perform conversion between speech and text in real time in language learning and support visual learning.SOLUTION: A system according to an embodiment includes a voice conversion unit, a text conversion unit, a real-time support unit, and a visual learning unit. The voice conversion unit converts voice into text. The text conversion unit converts the text converted by the voice conversion unit into voice. The real-time support unit supports English conversation learning in real time by using the voice conversion unit and the text conversion unit. The visual learner supports visual learning.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately support visual learning by converting speech to text in real time in language learning.

[0005] The system according to the embodiment aims to support visual learning in language learning by converting speech to text in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech conversion unit, a text conversion unit, a real-time support unit, and a visual learning unit. The speech conversion unit converts speech into text. The text conversion unit converts the text converted by the speech conversion unit into speech. The real-time support unit supports English conversation learning in real time using the speech conversion unit and the text conversion unit. The visual learning unit supports visual learning. [Effects of the Invention]

[0007] The system according to the embodiment can convert speech to text in real time and support visual learning in language learning. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A voice learning assistant according to an embodiment of the present invention is a system that converts speech to text and text to speech, supports English conversation learning in real time, and supports visual learning. This allows the voice learning assistant to support English conversation learning in real time through speech-to-text conversion and also enables visual learning.

[0029] A voice learning assistant according to an embodiment includes a speech conversion unit, a text conversion unit, a real-time support unit, and a visual learning unit. The speech conversion unit converts speech into text. For example, the speech conversion unit converts a user's speech into text using speech recognition technology. The speech conversion unit can also analyze speech based on an algorithm used and generate text data. The speech conversion unit can also remove background noise using noise reduction technology to achieve more accurate text conversion. The text conversion unit converts text into speech. For example, the text conversion unit converts text data into speech data using speech synthesis technology. The text conversion unit can also analyze text based on an algorithm used and generate natural-sounding speech. The text conversion unit can also have a function that allows the user to select a voice tone or accent according to their preferences. The real-time support unit supports English conversation learning in real time using the speech conversion unit and text conversion unit. For example, the real-time support unit can analyze the user's conversational fluency and provide feedback on areas for improvement. The real-time support unit can also understand the context of the user's conversation and provide appropriate responses. The real-time support unit can also use emotion estimation to suggest conversation topics based on the user's emotions, stimulating learning interest. The visual learning unit supports visual learning. For example, the visual learning unit uses OpenAI's DALL-E2 to visually represent the content the user is studying. The visual learning unit can also analyze the user's study history and suggest the most appropriate images. The visual learning unit can also add interactive elements to visual learning content, allowing users to manipulate images to study. This allows the voice learning assistant to support English conversation learning in real time through voice-to-text conversion, while also enabling visual learning.

[0030] The speech conversion unit can analyze the user's pronunciation characteristics and provide feedback on areas for improvement in pronunciation. For example, the speech conversion unit analyzes the user's speech and extracts pronunciation characteristics. For example, if the pronunciation of a specific phoneme is unclear, that part is emphasized and provided as feedback. The speech conversion unit also analyzes speech data and compares the user's pronunciation with standard pronunciation. For example, it evaluates the length of vowels and the strength of consonants and provides specific suggestions for improvement. The speech conversion unit also analyzes pronunciation characteristics and suggests specific practice methods to the user. For example, it provides audio guidance for practicing the pronunciation of specific phonemes. In this way, by providing feedback on areas for improvement in the user's pronunciation, it is possible to improve pronunciation.

[0031] The speech conversion unit analyzes background sounds included in the speech data and removes noise to enable more accurate text conversion. For example, the speech conversion unit analyzes speech data and uses an algorithm to separate background sounds from the user's speech. For example, the speech conversion unit removes background sounds using noise canceling technology. The speech conversion unit also analyzes the frequency spectrum of the speech data to identify and remove noise components. For example, it filters noise in a specific frequency band. The speech conversion unit also analyzes both the time domain and the frequency domain of the speech data to effectively remove noise. For example, it reduces noise components using a short-time Fourier transform. This removes noise, enabling more accurate text conversion.

[0032] The speech conversion unit can combine speech-to-text conversion with a translation function between different languages ​​to provide multilingual learning support. For example, the speech conversion unit converts a user's speech into text and translates the text into a different language. For example, it translates English speech into Japanese text. The speech conversion unit also performs speech-to-text conversion and translation in real time, allowing a user to practice conversation in different languages. For example, if you speak in English, you will receive a response in Japanese. The speech conversion unit also integrates speech recognition and translation functions that support multiple languages ​​to provide multilingual learning support. For example, it supports multiple languages ​​such as English, French, and Spanish. This makes it possible to provide multilingual learning support by combining the translation function between different languages.

[0033] The speech conversion unit displays the speech-to-text conversion result as subtitles in real time, thereby providing learning support for the hearing impaired. The speech conversion unit, for example, converts the speech spoken by a user into text and displays the text as subtitles in real time. For example, the content of the conversation is displayed on a screen. The speech conversion unit also displays the speech-to-text conversion result in a format customized for the hearing impaired. For example, it uses large fonts and color coding to improve visibility. The speech conversion unit also displays subtitles in real time, making it easier for the hearing impaired to understand the content of the conversation. For example, it scrolls the subtitles as the conversation progresses. This makes it possible to provide learning support for the hearing impaired by displaying subtitles in real time.

[0034] The text conversion unit may have a function that allows the user to select a voice tone or accent according to their preferences. For example, the text conversion unit provides an interface that allows the user to select a voice tone or accent when converting input text to speech. For example, the user can select a male voice, a female voice, or a different regional accent. The text conversion unit also automatically applies a voice tone or accent according to the user's preferences when converting text to speech. For example, the text conversion unit may suggest optimal settings based on the user's past selection history. The text conversion unit also provides a function that allows the user to customize their preferred voice tone or accent. For example, the text conversion unit may add an option to adjust the pitch and speed of the voice. This allows the user to select a voice tone or accent according to their preferences, thereby improving the learning experience.

[0035] The text conversion unit can compare the text-to-speech conversion result with the user's pronunciation and provide feedback on any differences in pronunciation. For example, the text conversion unit converts text entered by the user into speech and compares the speech with the user's pronunciation. For example, the speech spoken by the user and the generated speech are played side by side. The text conversion unit also analyzes the text-to-speech conversion result and identifies parts that differ from the user's pronunciation. For example, if the pronunciation of a specific phoneme is different, that part is emphasized and provided as feedback. The text conversion unit also suggests specific practice methods to the user in order to provide feedback on the differences in pronunciation. For example, it provides audio guidance for practicing the pronunciation of a specific phoneme. In this way, the user's pronunciation differences can be fed back to improve their pronunciation.

[0036] The text conversion unit can apply text-to-speech conversion to narrative or dialogue-style learning content to provide an interactive learning experience. For example, the text conversion unit uses text-to-speech conversion to provide narrative-style learning content. For example, text entered by a user is played aloud as part of a story. The text conversion unit also applies text-to-speech conversion to dialogue-style learning content to enable users to learn interactively. For example, a generative AI responds to text entered by a user. The text conversion unit also develops scenario-based learning content using text-to-speech conversion to provide an interactive learning experience. For example, a story progresses as the user selects options. This allows for an interactive learning experience to be provided by applying it to narrative or dialogue-style learning content.

[0037] The text conversion unit can combine the text-to-speech conversion results with music or sound effects to create highly entertaining learning content. For example, the text conversion unit can combine the text-to-speech conversion results with music to create highly entertaining learning content. For example, background music can be added to enhance the story. The text conversion unit can also combine sound effects with the text-to-speech conversion results to enhance the realism of the learning content. For example, sound effects can be played to match specific scenes. The text conversion unit can also use music or sound effects to provide the text-to-speech conversion results in a highly entertaining format. For example, text can be read rhythmically in time with music. In this way, by combining music and sound effects, highly entertaining learning content can be created.

[0038] The real-time support unit can analyze the user's conversational fluency and provide feedback on areas for improvement. The real-time support unit, for example, analyzes the user's conversation in real time and uses an algorithm to evaluate fluency. For example, it analyzes the speaking speed and pauses and provides feedback. The real-time support unit also analyzes the conversational fluency and suggests specific areas for improvement to the user. For example, it provides advice on adjusting the speaking speed. The real-time support unit also analyzes the user's conversational fluency during real-time English conversation learning and suggests practice methods. For example, it provides a guide for repeatedly practicing specific phrases. In this way, the user's conversational ability can be improved by analyzing the user's conversational fluency and providing feedback on areas for improvement.

[0039] The real-time support unit can understand the context of the user's conversation and provide an appropriate response. For example, the real-time support unit analyzes the user's conversation in real time and uses an algorithm to understand the context. For example, it analyzes the flow and topic of the conversation and generates an appropriate response. In addition, the real-time support unit uses a generation AI to understand the context of the user's conversation and provide a natural response. For example, if a user asks a question, it generates an appropriate answer. In addition, during real-time English conversation learning, the generation AI understands the context of the user's conversation and provides a response to smoothly progress the conversation. For example, it suggests the next topic in line with the flow of the conversation. This makes it possible to understand the context of the user's conversation and provide an appropriate response, thereby realizing a natural conversation.

[0040] The real-time support unit can expand real-time English conversation learning to a group discussion format, providing an environment in which multiple users can learn simultaneously. For example, the real-time support unit expands real-time English conversation learning to a group discussion format, building a system in which multiple users can participate simultaneously. For example, a group discussion is held using an online conference system. Furthermore, to support English conversation learning in a group discussion format, the generation AI analyzes each user's comments and provides appropriate feedback. For example, it evaluates the content and fluency of the comments. Furthermore, the real-time support unit expands real-time English conversation learning to a group discussion format, providing an environment in which multiple users can learn simultaneously. For example, it provides a space where users can exchange opinions with each other. By expanding the format to a group discussion, it is possible to provide an environment in which multiple users can learn simultaneously.

[0041] The real-time support unit conducts real-time English conversation learning in a virtual reality (VR) environment, thereby providing a more immersive learning experience. The real-time support unit, for example, builds a system for conducting real-time English conversation learning in a virtual reality (VR) environment. For example, a VR headset is used to realize conversations in a virtual space. Furthermore, to support English conversation learning in the virtual reality (VR) environment, the real-time support unit uses a generative AI to analyze real-time conversations and provide appropriate feedback. For example, it points out pronunciation and grammar errors. Furthermore, to provide a more immersive learning experience, the real-time support unit conducts real-time English conversation learning in a virtual reality (VR) environment. For example, it provides practical conversation practice through simulations in a virtual space. In this way, by providing learning in a virtual reality (VR) environment, a more immersive learning experience can be provided.

[0042] The visual learning unit can analyze the user's learning history and suggest optimal images. For example, the visual learning unit analyzes the user's learning history and suggests optimal images related to the learning content. For example, it displays images related to words and phrases that have been learned in the past. In addition, the visual learning unit uses a generation AI to automatically generate images to support visual learning based on the user's learning history. For example, it generates images that match the topic the user is studying. In addition, the visual learning unit analyzes the learning history and suggests the most effective visual learning content for the user. For example, it displays images related to words and phrases that the user has difficulty with. In this way, by analyzing the user's learning history and suggesting optimal images, the effectiveness of visual learning can be improved.

[0043] The visual learning unit can add interactive elements to the visual learning content, allowing the user to learn by manipulating images. For example, the visual learning unit can add interactive elements to the visual learning content, allowing the user to learn by manipulating images. For example, dragging and dropping images to learn vocabulary. The visual learning unit can also add interactive elements, allowing the user to learn while manipulating the visual learning content. For example, clicking on an image to display detailed information. The visual learning unit can also incorporate interactive elements into the visual learning content, allowing the user to actively participate in learning. For example, it can provide quizzes and games using images. In this way, adding interactive elements to the visual learning content allows the user to learn by manipulating images.

[0044] The visual learning unit can combine visual learning with augmented reality (AR) technology to overlay learning content on the real world. For example, the visual learning unit builds a system that combines visual learning content with augmented reality (AR) technology and overlays it on the real world. For example, the learning content is displayed through a smartphone camera. The visual learning unit also uses augmented reality (AR) technology to overlay the visual learning content on the real world. For example, it displays images related to the words the user is studying in the real world. The visual learning unit also overlays the learning content on the real world to make visual learning more effective. For example, it displays information related to the place or object the user is studying. In this way, the use of augmented reality (AR) technology allows the learning content to be overlaid on the real world, enhancing the effectiveness of visual learning.

[0045] The visual learning unit can combine visual learning content with images or videos created by the user to provide a personalized learning experience. For example, the visual learning unit can combine images or videos created by the user with the visual learning content to provide a personalized learning experience. For example, photos taken by the user can be used in the learning content. The visual learning unit can also personalize the learning experience by incorporating images or videos created by the user into the visual learning content. For example, videos created by the user can be used as learning materials. The visual learning unit can also enhance the effectiveness of learning by combining images or videos created by the user with the visual learning content. For example, images taken by the user can be used to learn vocabulary words. In this way, a personalized learning experience can be provided by combining images and videos created by the user.

[0046] The real-time support unit can analyze the learner's level of comprehension and provide an individually customized learning plan. The real-time support unit, for example, analyzes the learner's level of comprehension and provides an individually customized learning plan based on the results. For example, a plan that focuses on areas where the learner has low comprehension is created. The real-time support unit also analyzes the learner's progress data and provides an individually customized learning plan. For example, a plan that matches the learner's pace is created. The real-time support unit also analyzes the learner's level of comprehension in real time and dynamically adjusts the learning plan based on the results. For example, a plan that focuses on areas where the learner is weak is provided. In this way, by analyzing the learner's level of comprehension and providing an individually customized learning plan, it is possible to improve learning effectiveness.

[0047] The real-time support unit can monitor the learner's progress in real time and provide feedback at an appropriate time. For example, the real-time support unit monitors the learner's progress in real time and provides appropriate feedback based on the results. For example, feedback is provided when the learner completes a specific task. The real-time support unit also provides timely feedback to the learner through real-time progress monitoring. For example, an explanation is provided immediately for a problem that the learner got wrong. The real-time support unit also monitors the learner's progress in real time and provides feedback to maximize the effectiveness of learning. For example, praise is provided when the learner has mastered a specific skill. In this way, by monitoring the learner's progress in real time and providing feedback at an appropriate time, the effectiveness of learning can be improved.

[0048] The real-time support unit can add a ranking function that compares a learner's level of understanding with other learners and stimulates a competitive spirit. The real-time support unit adds, for example, a function that compares a learner's level of understanding with other learners and displays it in a ranking format. For example, the ranking reflects the learner's score and progress. The real-time support unit also displays the learner's level of understanding in a ranking format to stimulate a competitive spirit. For example, top learners are given special benefits. The real-time support unit also provides a ranking function that compares a learner's level of understanding with other learners and stimulates a competitive spirit. For example, learners can compete with each other to increase their motivation to learn. In this way, a learner's motivation to learn can be increased by comparing their level of understanding with other learners and stimulating a competitive spirit.

[0049] The real-time support unit can share the learner's progress data with parents or teachers to enhance learning support. The real-time support unit, for example, provides a function for sharing the learner's progress data with parents or teachers. For example, the real-time support unit periodically reports the learner's grades and progress. The real-time support unit also shares the learner's progress data with parents or teachers to enhance learning support. For example, the real-time support unit provides additional support in areas where the learner is weak. The real-time support unit also shares the learner's progress data with parents or teachers in real time to enhance learning support. For example, the real-time support unit sends a notification when the learner completes a specific assignment. This makes it possible to enhance learning support by sharing the learner's progress data with parents or teachers.

[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 conversion unit can analyze the user's pronunciation characteristics and provide feedback on areas for improvement. For example, it can analyze the user's speech and extract pronunciation characteristics. If the pronunciation of a particular phoneme is unclear, it can emphasize that part and provide feedback. The speech conversion unit can also analyze speech data and compare the user's pronunciation with standard pronunciation. It can also evaluate the length of vowels and the strength of consonants and provide specific suggestions for improvement. Furthermore, the speech conversion unit can analyze pronunciation characteristics and suggest specific practice methods to the user. It can also provide audio guidance for practicing the pronunciation of specific phonemes. This can improve pronunciation by providing feedback on areas for improvement to the user's pronunciation.

[0052] The speech conversion unit can analyze background sounds contained in the speech data and remove noise to enable more accurate text conversion. For example, it analyzes the speech data and uses an algorithm to separate background sounds from the user's voice. It can also remove background sounds using noise canceling technology. The speech conversion unit can also analyze the frequency spectrum of the speech data to identify and remove noise components. It can also filter noise in specific frequency bands. Furthermore, the speech conversion unit analyzes both the time domain and frequency domain of the speech data to effectively remove noise. It can also reduce noise components using a short-time Fourier transform. This allows for more accurate text conversion by removing noise.

[0053] The speech conversion unit can combine speech-to-text conversion with a translation function between different languages ​​to provide multilingual learning support. For example, it converts a user's speech into text and then translates the text into a different language. It is also possible to translate English speech into Japanese text. The speech conversion unit also performs speech-to-text conversion and translation in real time, allowing users to practice conversations in different languages. It is also possible to speak in English and receive a response in Japanese. Furthermore, the speech conversion unit integrates speech recognition and translation functions that support multiple languages ​​to provide multilingual learning support. It is also possible to support multiple languages, such as English, French, and Spanish. This makes it possible to provide multilingual learning support by combining the translation function between different languages.

[0054] The speech conversion unit can display the results of speech-to-text conversion as subtitles in real time, providing learning support for the hearing impaired. For example, it can convert the user's speech into text and display the text as subtitles in real time. It is also possible to display the content of the conversation on a screen. The speech conversion unit also displays the results of speech-to-text conversion in a format customized for the hearing impaired. Visibility can also be improved by using large fonts and color coding. Furthermore, the speech conversion unit displays subtitles in real time, making it easier for the hearing impaired to understand the content of the conversation. It is also possible to display subtitles by scrolling as the conversation progresses. This makes it possible to provide learning support for the hearing impaired by displaying subtitles in real time.

[0055] The text converter may have a function that allows the user to select a voice tone and accent according to their preferences. For example, when converting input text to speech, the converter may provide an interface that allows the user to select a voice tone and accent. It may also be possible to select a male voice, a female voice, or a different regional accent. The text converter may also automatically apply a voice tone and accent according to the user's preferences during text-to-speech conversion. It may also suggest optimal settings based on the user's past selection history. The text converter may also provide a function that allows the user to customize their preferred voice tone and accent. Options for adjusting the voice pitch and speed may also be added. This allows the user to select a voice tone and accent according to their preferences, thereby improving the learning experience.

[0056] The text conversion unit can compare the text-to-speech conversion result with the user's pronunciation and provide feedback on any differences in pronunciation. For example, it can convert text entered by the user into speech and compare that speech with the user's pronunciation. It can also play the user's speech and the generated speech side by side. The text conversion unit can also analyze the text-to-speech conversion result and identify parts that differ from the user's pronunciation. If the pronunciation of a specific phoneme is different, it can emphasize that part and provide feedback. Furthermore, the text conversion unit can suggest specific practice methods to the user to provide feedback on the differences in pronunciation. It can also provide audio guidance for practicing the pronunciation of specific phonemes. This allows the user's pronunciation to be improved by providing feedback on the differences in pronunciation.

[0057] The text conversion unit can apply text-to-speech conversion to narrative or dialogue-style learning content to provide an interactive learning experience. For example, text-to-speech conversion can be used to provide narrative learning content. Text entered by a user can be played aloud as part of the story. The text conversion unit can also apply text-to-speech conversion to dialogue-style learning content to enable users to learn interactively. A generative AI can also respond to text entered by a user. Furthermore, the text conversion unit can develop scenario-based learning content using text-to-speech conversion to provide an interactive learning experience. The story can also progress as the user selects options. This makes it possible to provide an interactive learning experience by applying text-to-speech conversion to narrative or dialogue-style learning content.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The speech conversion unit converts speech into text. For example, the speech conversion unit converts the user's speech into text using speech recognition technology. The speech conversion unit can also analyze the speech based on an algorithm used to generate text data. Furthermore, the speech conversion unit can also use noise reduction technology to remove background sounds and perform more accurate text conversion. Step 2: The text conversion unit converts the text into speech. For example, the text conversion unit converts text data into speech data using speech synthesis technology. The text conversion unit can also analyze the text based on an algorithm used to generate natural-sounding speech. Furthermore, the text conversion unit can also have a function that allows the user to select a voice tone or accent according to their preferences. Step 3: The real-time support unit uses the speech conversion unit and text conversion unit to support English conversation learning in real time. For example, the real-time support unit analyzes the user's conversational fluency and provides feedback on areas for improvement. The real-time support unit can also understand the context of the user's conversation and provide appropriate responses. Furthermore, the real-time support unit can use an emotion estimation function to suggest conversation topics based on the user's emotions, thereby stimulating learning interest. Step 4: The visual learning unit supports visual learning. For example, the visual learning unit uses OpenAI's DALL-E2 to visually represent the content the user is learning. The visual learning unit can also analyze the user's learning history and suggest the most suitable images. Furthermore, the visual learning unit can add interactive elements to the visual learning content, allowing users to manipulate the images to learn.

[0060] (Example 2) A voice learning assistant according to an embodiment of the present invention is a system that converts speech to text and text to speech, supports English conversation learning in real time, and supports visual learning. This allows the voice learning assistant to support English conversation learning in real time through speech-to-text conversion and also enables visual learning.

[0061] A voice learning assistant according to an embodiment includes a speech conversion unit, a text conversion unit, a real-time support unit, and a visual learning unit. The speech conversion unit converts speech into text. For example, the speech conversion unit converts a user's speech into text using speech recognition technology. The speech conversion unit can also analyze speech based on an algorithm used and generate text data. The speech conversion unit can also remove background noise using noise reduction technology to achieve more accurate text conversion. The text conversion unit converts text into speech. For example, the text conversion unit converts text data into speech data using speech synthesis technology. The text conversion unit can also analyze text based on an algorithm used and generate natural-sounding speech. The text conversion unit can also have a function that allows the user to select a voice tone or accent according to their preferences. The real-time support unit supports English conversation learning in real time using the speech conversion unit and text conversion unit. For example, the real-time support unit can analyze the user's conversational fluency and provide feedback on areas for improvement. The real-time support unit can also understand the context of the user's conversation and provide appropriate responses. The real-time support unit can also use emotion estimation to suggest conversation topics based on the user's emotions, stimulating learning interest. The visual learning unit supports visual learning. For example, the visual learning unit uses OpenAI's DALL-E2 to visually represent the content the user is studying. The visual learning unit can also analyze the user's study history and suggest the most appropriate images. The visual learning unit can also add interactive elements to visual learning content, allowing users to manipulate images to study. This allows the voice learning assistant to support English conversation learning in real time through voice-to-text conversion, while also enabling visual learning.

[0062] The speech conversion unit can analyze the user's pronunciation characteristics and provide feedback on areas for improvement in pronunciation. For example, the speech conversion unit analyzes the user's speech and extracts pronunciation characteristics. For example, if the pronunciation of a specific phoneme is unclear, that part is emphasized and provided as feedback. The speech conversion unit also analyzes speech data and compares the user's pronunciation with standard pronunciation. For example, it evaluates the length of vowels and the strength of consonants and provides specific suggestions for improvement. The speech conversion unit also analyzes pronunciation characteristics and suggests specific practice methods to the user. For example, it provides audio guidance for practicing the pronunciation of specific phonemes. In this way, by providing feedback on areas for improvement in the user's pronunciation, it is possible to improve pronunciation.

[0063] The speech conversion unit analyzes background sounds included in the speech data and removes noise to enable more accurate text conversion. For example, the speech conversion unit analyzes speech data and uses an algorithm to separate background sounds from the user's speech. For example, the speech conversion unit removes background sounds using noise canceling technology. The speech conversion unit also analyzes the frequency spectrum of the speech data to identify and remove noise components. For example, it filters noise in a specific frequency band. The speech conversion unit also analyzes both the time domain and the frequency domain of the speech data to effectively remove noise. For example, it reduces noise components using a short-time Fourier transform. This removes noise, enabling more accurate text conversion.

[0064] The voice conversion unit can use the emotion estimation function to analyze the user's emotions and provide feedback according to the emotions. The voice conversion unit, for example, analyzes the user's voice data and uses an algorithm to estimate emotions. For example, it identifies emotions by analyzing the tone and pitch of the voice. The voice conversion unit also provides appropriate feedback to the user based on the emotion estimation result. For example, if the user is nervous, it provides advice to relax. The voice conversion unit also uses the emotion estimation function to provide feedback to increase the user's motivation to learn. For example, it displays an encouraging message to a user who has positive emotions. In this way, by providing feedback according to the user's emotions, it is possible to increase the user's motivation to learn.

[0065] The speech conversion unit can combine speech-to-text conversion with a translation function between different languages ​​to provide multilingual learning support. For example, the speech conversion unit converts a user's speech into text and translates the text into a different language. For example, it translates English speech into Japanese text. The speech conversion unit also performs speech-to-text conversion and translation in real time, allowing a user to practice conversation in different languages. For example, if you speak in English, you will receive a response in Japanese. The speech conversion unit also integrates speech recognition and translation functions that support multiple languages ​​to provide multilingual learning support. For example, it supports multiple languages ​​such as English, French, and Spanish. This makes it possible to provide multilingual learning support by combining the translation function between different languages.

[0066] The speech conversion unit displays the speech-to-text conversion result as subtitles in real time, thereby providing learning support for the hearing impaired. The speech conversion unit, for example, converts the speech spoken by a user into text and displays the text as subtitles in real time. For example, the content of the conversation is displayed on a screen. The speech conversion unit also displays the speech-to-text conversion result in a format customized for the hearing impaired. For example, it uses large fonts and color coding to improve visibility. The speech conversion unit also displays subtitles in real time, making it easier for the hearing impaired to understand the content of the conversation. For example, it scrolls the subtitles as the conversation progresses. This makes it possible to provide learning support for the hearing impaired by displaying subtitles in real time.

[0067] The voice conversion unit uses an emotion estimation function to automatically generate learning content according to the user's emotions, thereby increasing motivation to learn. The voice conversion unit, for example, analyzes the user's voice data and uses an algorithm to estimate emotions. For example, it identifies emotions by analyzing the tone and pitch of the voice. The voice conversion unit also automatically generates learning content appropriate for the user based on the emotion estimation results. For example, if the user has positive emotions, it provides more difficult tasks. The voice conversion unit also uses the emotion estimation function to provide content to increase the user's motivation to learn. For example, if the user has negative emotions, it provides encouraging messages or easy tasks. In this way, by providing learning content according to the user's emotions, it is possible to increase motivation to learn.

[0068] The text conversion unit may have a function that allows the user to select a voice tone or accent according to their preferences. For example, the text conversion unit provides an interface that allows the user to select a voice tone or accent when converting input text to speech. For example, the user can select a male voice, a female voice, or a different regional accent. The text conversion unit also automatically applies a voice tone or accent according to the user's preferences when converting text to speech. For example, the text conversion unit may suggest optimal settings based on the user's past selection history. The text conversion unit also provides a function that allows the user to customize their preferred voice tone or accent. For example, the text conversion unit may add an option to adjust the pitch and speed of the voice. This allows the user to select a voice tone or accent according to their preferences, thereby improving the learning experience.

[0069] The text conversion unit can compare the text-to-speech conversion result with the user's pronunciation and provide feedback on any differences in pronunciation. For example, the text conversion unit converts text entered by the user into speech and compares the speech with the user's pronunciation. For example, the speech spoken by the user and the generated speech are played side by side. The text conversion unit also analyzes the text-to-speech conversion result and identifies parts that differ from the user's pronunciation. For example, if the pronunciation of a specific phoneme is different, that part is emphasized and provided as feedback. The text conversion unit also suggests specific practice methods to the user in order to provide feedback on the differences in pronunciation. For example, it provides audio guidance for practicing the pronunciation of a specific phoneme. In this way, the user's pronunciation differences can be fed back to improve their pronunciation.

[0070] The text conversion unit can use the emotion estimation function to reflect emotional expressions in the voice according to the content of the text. The text conversion unit, for example, analyzes the content of the text and uses an algorithm to estimate emotions. For example, it identifies emotional expressions contained in the text and reflects those emotions in the voice. The text conversion unit also applies appropriate emotional expressions when converting text to voice based on the emotion estimation results. For example, it generates a bright-toned voice for text containing the emotion of joy. The text conversion unit also uses the emotion estimation function to reflect emotional expressions in the voice according to the content of the text. For example, it generates a calm-toned voice for text containing the emotion of sadness. In this way, by reflecting emotional expressions in the voice according to the content of the text, more natural voice output is possible.

[0071] The text conversion unit can apply text-to-speech conversion to narrative or dialogue-style learning content to provide an interactive learning experience. For example, the text conversion unit uses text-to-speech conversion to provide narrative-style learning content. For example, text entered by a user is played aloud as part of a story. The text conversion unit also applies text-to-speech conversion to dialogue-style learning content to enable users to learn interactively. For example, a generative AI responds to text entered by a user. The text conversion unit also develops scenario-based learning content using text-to-speech conversion to provide an interactive learning experience. For example, a story progresses as the user selects options. This allows for an interactive learning experience to be provided by applying it to narrative or dialogue-style learning content.

[0072] The text conversion unit can combine the text-to-speech conversion results with music or sound effects to create highly entertaining learning content. For example, the text conversion unit can combine the text-to-speech conversion results with music to create highly entertaining learning content. For example, background music can be added to enhance the story. The text conversion unit can also combine sound effects with the text-to-speech conversion results to enhance the realism of the learning content. For example, sound effects can be played to match specific scenes. The text conversion unit can also use music or sound effects to provide the text-to-speech conversion results in a highly entertaining format. For example, text can be read rhythmically in time with music. In this way, by combining music and sound effects, highly entertaining learning content can be created.

[0073] The text conversion unit uses the emotion estimation function to provide voice feedback according to the user's emotion, thereby maintaining motivation for learning. The text conversion unit, for example, estimates the user's emotion and provides voice feedback based on the result. For example, if the user has positive emotions, it provides an encouraging message by voice. The text conversion unit also uses the emotion estimation function to provide voice feedback according to the user's emotion in real time. For example, if the user has negative emotions, it provides voice advice to relax. The text conversion unit also maintains motivation for learning by providing voice feedback according to the user's emotion. For example, if the user is tired, it provides a voice message encouraging the user to take a break. In this way, by providing voice feedback according to the user's emotion, it is possible to maintain motivation for learning.

[0074] The real-time support unit can analyze the user's conversational fluency and provide feedback on areas for improvement. The real-time support unit, for example, analyzes the user's conversation in real time and uses an algorithm to evaluate fluency. For example, it analyzes the speaking speed and pauses and provides feedback. The real-time support unit also analyzes the conversational fluency and suggests specific areas for improvement to the user. For example, it provides advice on adjusting the speaking speed. The real-time support unit also analyzes the user's conversational fluency during real-time English conversation learning and suggests practice methods. For example, it provides a guide for repeatedly practicing specific phrases. In this way, the user's conversational ability can be improved by analyzing the user's conversational fluency and providing feedback on areas for improvement.

[0075] The real-time support unit can understand the context of the user's conversation and provide an appropriate response. For example, the real-time support unit analyzes the user's conversation in real time and uses an algorithm to understand the context. For example, it analyzes the flow and topic of the conversation and generates an appropriate response. In addition, the real-time support unit uses a generation AI to understand the context of the user's conversation and provide a natural response. For example, if a user asks a question, it generates an appropriate answer. In addition, during real-time English conversation learning, the generation AI understands the context of the user's conversation and provides a response to smoothly progress the conversation. For example, it suggests the next topic in line with the flow of the conversation. This makes it possible to understand the context of the user's conversation and provide an appropriate response, thereby realizing a natural conversation.

[0076] The real-time support unit uses the emotion estimation function to suggest conversation topics that correspond to the user's emotions, thereby stimulating learning interest. The real-time support unit, for example, analyzes the user's emotions in real time and suggests conversation topics based on the results. For example, it selects topics that are likely to interest the user. The real-time support unit also uses the emotion estimation function to suggest conversation topics that correspond to the user's emotions. For example, if the user has positive emotions, it provides fun topics. The real-time support unit also dynamically adjusts conversation topics based on the user's emotions. For example, if the user is tired, it suggests topics that will help the user relax. In this way, it is possible to stimulate learning interest by suggesting conversation topics that correspond to the user's emotions.

[0077] The real-time support unit can expand real-time English conversation learning to a group discussion format, providing an environment in which multiple users can learn simultaneously. For example, the real-time support unit expands real-time English conversation learning to a group discussion format, building a system in which multiple users can participate simultaneously. For example, a group discussion is held using an online conference system. Furthermore, to support English conversation learning in a group discussion format, the generation AI analyzes each user's comments and provides appropriate feedback. For example, it evaluates the content and fluency of the comments. Furthermore, the real-time support unit expands real-time English conversation learning to a group discussion format, providing an environment in which multiple users can learn simultaneously. For example, it provides a space where users can exchange opinions with each other. By expanding the format to a group discussion, it is possible to provide an environment in which multiple users can learn simultaneously.

[0078] The real-time support unit conducts real-time English conversation learning in a virtual reality (VR) environment, thereby providing a more immersive learning experience. The real-time support unit, for example, builds a system for conducting real-time English conversation learning in a virtual reality (VR) environment. For example, a VR headset is used to realize conversations in a virtual space. Furthermore, to support English conversation learning in the virtual reality (VR) environment, the real-time support unit uses a generative AI to analyze real-time conversations and provide appropriate feedback. For example, it points out pronunciation and grammar errors. Furthermore, to provide a more immersive learning experience, the real-time support unit conducts real-time English conversation learning in a virtual reality (VR) environment. For example, it provides practical conversation practice through simulations in a virtual space. In this way, by providing learning in a virtual reality (VR) environment, a more immersive learning experience can be provided.

[0079] The real-time support unit can use the emotion estimation function to adjust the difficulty of the conversation according to the user's emotions and provide an appropriate learning challenge. The real-time support unit, for example, analyzes the user's emotions in real time and adjusts the difficulty of the conversation based on the results. For example, if the user is relaxed, a more difficult conversation is provided. The real-time support unit also uses the emotion estimation function to dynamically adjust the difficulty of the conversation according to the user's emotions. For example, if the user is nervous, an easy conversation is provided. The real-time support unit also adjusts the difficulty of the conversation to provide an appropriate learning challenge based on the user's emotions. For example, if the user has positive emotions, a challenging task is provided. In this way, an appropriate learning challenge can be provided by adjusting the difficulty of the conversation according to the user's emotions.

[0080] The visual learning unit can analyze the user's learning history and suggest optimal images. For example, the visual learning unit analyzes the user's learning history and suggests optimal images related to the learning content. For example, it displays images related to words and phrases that have been learned in the past. In addition, the visual learning unit uses a generation AI to automatically generate images to support visual learning based on the user's learning history. For example, it generates images that match the topic the user is studying. In addition, the visual learning unit analyzes the learning history and suggests the most effective visual learning content for the user. For example, it displays images related to words and phrases that the user has difficulty with. In this way, by analyzing the user's learning history and suggesting optimal images, the effectiveness of visual learning can be improved.

[0081] The visual learning unit can add interactive elements to the visual learning content, allowing the user to learn by manipulating images. For example, the visual learning unit can add interactive elements to the visual learning content, allowing the user to learn by manipulating images. For example, dragging and dropping images to learn vocabulary. The visual learning unit can also add interactive elements, allowing the user to learn while manipulating the visual learning content. For example, clicking on an image to display detailed information. The visual learning unit can also incorporate interactive elements into the visual learning content, allowing the user to actively participate in learning. For example, it can provide quizzes and games using images. In this way, adding interactive elements to the visual learning content allows the user to learn by manipulating images.

[0082] The visual learning unit uses the emotion estimation function to provide visual feedback according to the user's emotions, thereby enhancing the effectiveness of learning. The visual learning unit, for example, analyzes the user's emotions in real time and provides visual feedback based on the results. For example, if the user has positive emotions, it displays an encouraging message. The visual learning unit also uses the emotion estimation function to provide visual feedback according to the user's emotions. For example, if the user has negative emotions, it displays an image to help the user relax. The visual learning unit also dynamically adjusts the visual feedback based on the user's emotions. For example, if the user is tired, it displays a visual message encouraging the user to take a break. In this way, by providing visual feedback according to the user's emotions, it is possible to enhance the effectiveness of learning.

[0083] The visual learning unit can combine visual learning with augmented reality (AR) technology to overlay learning content on the real world. For example, the visual learning unit builds a system that combines visual learning content with augmented reality (AR) technology and overlays it on the real world. For example, the learning content is displayed through a smartphone camera. The visual learning unit also uses augmented reality (AR) technology to overlay the visual learning content on the real world. For example, it displays images related to the words the user is studying in the real world. The visual learning unit also overlays the learning content on the real world to make visual learning more effective. For example, it displays information related to the place or object the user is studying. In this way, the use of augmented reality (AR) technology allows the learning content to be overlaid on the real world, enhancing the effectiveness of visual learning.

[0084] The visual learning unit can combine visual learning content with images or videos created by the user to provide a personalized learning experience. For example, the visual learning unit can combine images or videos created by the user with the visual learning content to provide a personalized learning experience. For example, photos taken by the user can be used in the learning content. The visual learning unit can also personalize the learning experience by incorporating images or videos created by the user into the visual learning content. For example, videos created by the user can be used as learning materials. The visual learning unit can also enhance the effectiveness of learning by combining images or videos created by the user with the visual learning content. For example, images taken by the user can be used to learn vocabulary words. In this way, a personalized learning experience can be provided by combining images and videos created by the user.

[0085] The visual learning unit uses an emotion estimation function to automatically generate visual learning content according to the user's emotions, thereby stimulating learning interest. The visual learning unit, for example, analyzes the user's emotions in real time and automatically generates visual learning content based on the results. For example, if the user has positive emotions, it generates fun images. The visual learning unit also uses the emotion estimation function to provide visual learning content according to the user's emotions. For example, if the user has negative emotions, it generates relaxing images. The visual learning unit also dynamically generates visual learning content based on the user's emotions. For example, it generates images related to a topic that the user is likely to be interested in. In this way, the automatic generation of visual learning content according to the user's emotions can stimulate learning interest.

[0086] The real-time support unit can analyze the learner's level of comprehension and provide an individually customized learning plan. The real-time support unit, for example, analyzes the learner's level of comprehension and provides an individually customized learning plan based on the results. For example, a plan that focuses on areas where the learner has low comprehension is created. The real-time support unit also analyzes the learner's progress data and provides an individually customized learning plan. For example, a plan that matches the learner's pace is created. The real-time support unit also analyzes the learner's level of comprehension in real time and dynamically adjusts the learning plan based on the results. For example, a plan that focuses on areas where the learner is weak is provided. In this way, by analyzing the learner's level of comprehension and providing an individually customized learning plan, it is possible to improve learning effectiveness.

[0087] The real-time support unit can monitor the learner's progress in real time and provide feedback at an appropriate time. For example, the real-time support unit monitors the learner's progress in real time and provides appropriate feedback based on the results. For example, feedback is provided when the learner completes a specific task. The real-time support unit also provides timely feedback to the learner through real-time progress monitoring. For example, an explanation is provided immediately for a problem that the learner got wrong. The real-time support unit also monitors the learner's progress in real time and provides feedback to maximize the effectiveness of learning. For example, praise is provided when the learner has mastered a specific skill. In this way, by monitoring the learner's progress in real time and providing feedback at an appropriate time, the effectiveness of learning can be improved.

[0088] The real-time support unit can provide feedback to improve motivation according to the learner's emotions using the emotion estimation function. The real-time support unit, for example, analyzes the learner's emotions in real time and provides feedback to improve motivation based on the results. For example, if the learner has positive emotions, it displays an encouraging message. The real-time support unit also uses the emotion estimation function to provide feedback according to the learner's emotions. For example, if the learner has negative emotions, it provides advice to relax. The real-time support unit also dynamically adjusts the feedback to improve motivation based on the learner's emotions. For example, if the learner is tired, it displays a message encouraging the learner to take a break. In this way, by providing feedback to improve motivation according to the learner's emotions, it is possible to increase the learner's motivation to learn.

[0089] The real-time support unit can add a ranking function that compares a learner's level of understanding with other learners and stimulates a competitive spirit. The real-time support unit adds, for example, a function that compares a learner's level of understanding with other learners and displays it in a ranking format. For example, the ranking reflects the learner's score and progress. The real-time support unit also displays the learner's level of understanding in a ranking format to stimulate a competitive spirit. For example, top learners are given special benefits. The real-time support unit also provides a ranking function that compares a learner's level of understanding with other learners and stimulates a competitive spirit. For example, learners can compete with each other to increase their motivation to learn. In this way, a learner's motivation to learn can be increased by comparing their level of understanding with other learners and stimulating a competitive spirit.

[0090] The real-time support unit can share the learner's progress data with parents or teachers to enhance learning support. The real-time support unit, for example, provides a function for sharing the learner's progress data with parents or teachers. For example, the real-time support unit periodically reports the learner's grades and progress. The real-time support unit also shares the learner's progress data with parents or teachers to enhance learning support. For example, the real-time support unit provides additional support in areas where the learner is weak. The real-time support unit also shares the learner's progress data with parents or teachers in real time to enhance learning support. For example, the real-time support unit sends a notification when the learner completes a specific assignment. This makes it possible to enhance learning support by sharing the learner's progress data with parents or teachers.

[0091] The real-time support unit can use the emotion estimation function to suggest learning content that corresponds to the learner's emotions, thereby maximizing the effectiveness of learning. The real-time support unit, for example, analyzes the learner's emotions in real time and suggests learning content based on the results. For example, if the learner has positive emotions, it provides difficult tasks. The real-time support unit also uses the emotion estimation function to provide learning content that corresponds to the learner's emotions. For example, if the learner has negative emotions, it provides relaxing content. The real-time support unit also suggests content to maximize the effectiveness of learning based on the learner's emotions. For example, it provides content related to topics that the learner is likely to be interested in. In this way, the effectiveness of learning can be maximized by suggesting learning content that corresponds to the learner's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The speech conversion unit can analyze the user's pronunciation characteristics and provide feedback on areas for improvement. For example, it can analyze the user's speech and extract pronunciation characteristics. If the pronunciation of a particular phoneme is unclear, it can emphasize that part and provide feedback. The speech conversion unit can also analyze speech data and compare the user's pronunciation with standard pronunciation. It can also evaluate the length of vowels and the strength of consonants and provide specific suggestions for improvement. Furthermore, the speech conversion unit can analyze pronunciation characteristics and suggest specific practice methods to the user. It can also provide audio guidance for practicing the pronunciation of specific phonemes. This can improve pronunciation by providing feedback on areas for improvement to the user's pronunciation.

[0094] The speech conversion unit can analyze background sounds contained in the speech data and remove noise to enable more accurate text conversion. For example, it analyzes the speech data and uses an algorithm to separate background sounds from the user's voice. It can also remove background sounds using noise canceling technology. The speech conversion unit can also analyze the frequency spectrum of the speech data to identify and remove noise components. It can also filter noise in specific frequency bands. Furthermore, the speech conversion unit analyzes both the time domain and frequency domain of the speech data to effectively remove noise. It can also reduce noise components using a short-time Fourier transform. This allows for more accurate text conversion by removing noise.

[0095] The voice conversion unit can use the emotion estimation function to analyze the user's emotions and provide feedback according to the emotions. For example, an algorithm is used to analyze the user's voice data and estimate emotions. Emotions can be identified by analyzing the tone and pitch of the voice. The voice conversion unit also provides appropriate feedback to the user based on the emotion estimation results. If the user is nervous, it can also provide advice on how to relax. Furthermore, the voice conversion unit uses the emotion estimation function to provide feedback to increase the user's motivation to learn. It is also possible to display an encouraging message to a user who has positive emotions. In this way, by providing feedback according to the user's emotions, it is possible to increase motivation to learn.

[0096] The speech conversion unit can combine speech-to-text conversion with a translation function between different languages ​​to provide multilingual learning support. For example, it converts a user's speech into text and then translates the text into a different language. It is also possible to translate English speech into Japanese text. The speech conversion unit also performs speech-to-text conversion and translation in real time, allowing users to practice conversations in different languages. It is also possible to speak in English and receive a response in Japanese. Furthermore, the speech conversion unit integrates speech recognition and translation functions that support multiple languages ​​to provide multilingual learning support. It is also possible to support multiple languages, such as English, French, and Spanish. This makes it possible to provide multilingual learning support by combining the translation function between different languages.

[0097] The speech conversion unit can display the results of speech-to-text conversion as subtitles in real time, providing learning support for the hearing impaired. For example, it can convert the user's speech into text and display the text as subtitles in real time. It is also possible to display the content of the conversation on a screen. The speech conversion unit also displays the results of speech-to-text conversion in a format customized for the hearing impaired. Visibility can also be improved by using large fonts and color coding. Furthermore, the speech conversion unit displays subtitles in real time, making it easier for the hearing impaired to understand the content of the conversation. It is also possible to display subtitles by scrolling as the conversation progresses. This makes it possible to provide learning support for the hearing impaired by displaying subtitles in real time.

[0098] The voice conversion unit can use the emotion estimation function to automatically generate learning content according to the user's emotions, thereby increasing motivation to learn. For example, an algorithm is used to analyze the user's voice data and estimate emotions. Emotions can be identified by analyzing the tone and pitch of the voice. The voice conversion unit also automatically generates learning content appropriate for the user based on the emotion estimation results. If the user has positive emotions, it can provide more difficult tasks. Furthermore, the voice conversion unit uses the emotion estimation function to provide content that increases the user's motivation to learn. If the user has negative emotions, it can provide encouraging messages or easy tasks. In this way, learning content that matches the user's emotions can be provided, thereby increasing motivation to learn.

[0099] The text converter may have a function that allows the user to select a voice tone and accent according to their preferences. For example, when converting input text to speech, the converter may provide an interface that allows the user to select a voice tone and accent. It may also be possible to select a male voice, a female voice, or a different regional accent. The text converter may also automatically apply a voice tone and accent according to the user's preferences during text-to-speech conversion. It may also suggest optimal settings based on the user's past selection history. The text converter may also provide a function that allows the user to customize their preferred voice tone and accent. Options for adjusting the voice pitch and speed may also be added. This allows the user to select a voice tone and accent according to their preferences, thereby improving the learning experience.

[0100] The text conversion unit can compare the text-to-speech conversion result with the user's pronunciation and provide feedback on any differences in pronunciation. For example, it can convert text entered by the user into speech and compare that speech with the user's pronunciation. It can also play the user's speech and the generated speech side by side. The text conversion unit can also analyze the text-to-speech conversion result and identify parts that differ from the user's pronunciation. If the pronunciation of a specific phoneme is different, it can emphasize that part and provide feedback. Furthermore, the text conversion unit can suggest specific practice methods to the user to provide feedback on the differences in pronunciation. It can also provide audio guidance for practicing the pronunciation of specific phonemes. This allows the user's pronunciation to be improved by providing feedback on the differences in pronunciation.

[0101] The text conversion unit can use an emotion estimation function to reflect emotional expressions in the voice according to the content of the text. For example, it can analyze the content of the text and use an algorithm to estimate emotions. It can identify emotional expressions contained in the text and reflect those emotions in the voice. The text conversion unit also applies appropriate emotional expressions when converting text to voice based on the emotion estimation results. It can also generate a bright tone of voice for text containing the emotion of joy. Furthermore, the text conversion unit can use the emotion estimation function to reflect emotional expressions in the voice according to the content of the text. It can also generate a calm tone of voice for text containing the emotion of sadness. This allows emotional expressions in the voice according to the content of the text to be reflected, enabling more natural voice output.

[0102] The text conversion unit can apply text-to-speech conversion to narrative or dialogue-style learning content to provide an interactive learning experience. For example, text-to-speech conversion can be used to provide narrative learning content. Text entered by a user can be played aloud as part of the story. The text conversion unit can also apply text-to-speech conversion to dialogue-style learning content to enable users to learn interactively. A generative AI can also respond to text entered by a user. Furthermore, the text conversion unit can develop scenario-based learning content using text-to-speech conversion to provide an interactive learning experience. The story can also progress as the user selects options. This makes it possible to provide an interactive learning experience by applying text-to-speech conversion to narrative or dialogue-style learning content.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The speech conversion unit converts speech into text. For example, the speech conversion unit converts the user's speech into text using speech recognition technology. The speech conversion unit can also analyze the speech based on an algorithm used to generate text data. Furthermore, the speech conversion unit can also use noise reduction technology to remove background sounds and perform more accurate text conversion. Step 2: The text conversion unit converts the text into speech. For example, the text conversion unit converts text data into speech data using speech synthesis technology. The text conversion unit can also analyze the text based on an algorithm used to generate natural-sounding speech. Furthermore, the text conversion unit can also have a function that allows the user to select a voice tone or accent according to their preferences. Step 3: The real-time support unit uses the speech conversion unit and text conversion unit to support English conversation learning in real time. For example, the real-time support unit analyzes the user's conversational fluency and provides feedback on areas for improvement. The real-time support unit can also understand the context of the user's conversation and provide appropriate responses. Furthermore, the real-time support unit can use an emotion estimation function to suggest conversation topics based on the user's emotions, thereby stimulating learning interest. Step 4: The visual learning unit supports visual learning. For example, the visual learning unit uses OpenAI's DALL-E2 to visually represent the content the user is learning. The visual learning unit can also analyze the user's learning history and suggest the most suitable images. Furthermore, the visual learning unit can add interactive elements to the visual learning content, allowing users to manipulate the images to learn.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] 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.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] 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.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[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 speech conversion unit that converts speech into text; a text conversion unit that converts the text converted by the speech conversion unit into speech; a real-time support unit that supports English conversation learning in real time using the speech conversion unit and the text conversion unit; A visual learning section that supports visual learning. A system characterized by:

2. The voice conversion unit Analyzes the user's pronunciation characteristics and provides feedback on how to improve pronunciation 2. The system of claim 1.

3. The voice conversion unit Analyzes background sounds contained in audio data and removes noise for more accurate text conversion 2. The system of claim 1.

4. The voice conversion unit Analyze user emotions and provide feedback according to their emotions 2. The system of claim 1.

5. The voice conversion unit The speech-to-text conversion is combined with translation between different languages ​​to provide multilingual learning support.

2. The system of claim 1.

6. The voice conversion unit The results of the conversion from the voice to the text are displayed as subtitles in real time to provide learning support for the hearing impaired.

2. The system of claim 1.

7. The voice conversion unit Automatically generate learning content based on user emotions to increase motivation to learn 2. The system of claim 1.

8. The text conversion unit Equipped with a function that allows users to select the tone and accent of their voice according to their preferences 2. The system of claim 1.

Images