System

Abstract

An object of a system according to an embodiment is to enable a user to learn pronunciation of another language step by step.SOLUTION: A system includes a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives information on a native language of a user and a language to be learned. The generation unit specifically explains an intermediate pronunciation between the native language and the language to be learned based on the information received by the reception unit. The analysis unit analyzes the user's pronunciation based on the intermediate pronunciation generated by the generation unit. The providing unit provides feedback based on a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology assumes that foreign language pronunciation training requires mastering perfect pronunciation all at once, which makes it difficult for many users to keep up.

[0005] The system according to the embodiment aims to enable a user to gradually learn the pronunciation of another language. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, an analyzing unit, and a providing unit. The receiving unit receives information on the user's native language and the language to be learned. The generating unit explains a specific intermediate pronunciation between the native language and the language to be learned based on the information received by the receiving unit. The analyzing unit analyzes the user's pronunciation based on the intermediate pronunciation generated by the generating unit. The providing unit provides feedback based on the analysis result by the analyzing unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to learn pronunciation of another language step by step. [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 pronunciation correction system according to an embodiment of the present invention generates a pronunciation intermediate between a user's native language and a language to be learned, and gradually teaches native pronunciation. The pronunciation correction system accepts information about the user's native language and the language to be learned, generates the intermediate pronunciation using a generation AI (e.g., LLM), analyzes the user's pronunciation, and provides feedback. For example, the pronunciation correction system inputs the user's native language and the language to be learned. For example, the user simply selects their native language and the language to be learned. Next, the pronunciation correction system uses the generation AI to generate the intermediate pronunciation based on audio data of the native language and the language to be learned. For example, if the generation AI is to generate a pronunciation intermediate between Japanese and English, it analyzes Japanese audio data and English audio data and generates the intermediate pronunciation. Next, the pronunciation correction system gradually changes the generated intermediate pronunciation according to the user's proficiency level. For example, beginners are provided with a pronunciation closer to their native language, intermediate pronunciations are provided to intermediate learners, and pronunciation closer to native pronunciations is provided to advanced learners. Next, the pronunciation correction system analyzes the user's pronunciation and provides feedback as the user practices pronunciation. For example, by analyzing the user's pronunciation and pointing out any discrepancies from the correct pronunciation, the user can correct their pronunciation. This allows the pronunciation correction system to smoothly correct the user's pronunciation and acquire native pronunciation. This allows the pronunciation correction system to smoothly correct the user's pronunciation and acquire native pronunciation. For example, if a user whose native language is Japanese is learning English, by gradually learning intermediate pronunciation, the user can eventually acquire native English pronunciation. This allows the user to effectively learn pronunciation without having to give up on pronunciation practice.

[0029] A pronunciation correction system according to an embodiment includes a receiving unit, a generating unit, an analyzing unit, and a providing unit. The receiving unit receives information about a user's native language and a language to learn. The user simply selects, for example, their native language and the language to learn. The generating unit uses a generation AI to generate a pronunciation that is intermediate between the native language and the language to learn based on the information received by the receiving unit. The generation AI generates the intermediate pronunciation using, for example, a text generation AI (e.g., LLM). The generating unit can also generate the intermediate pronunciation based on audio data of the native language and the language to learn. For example, the generation AI analyzes audio data of Japanese and English and generates an intermediate pronunciation. The analyzing unit analyzes the user's pronunciation based on the intermediate pronunciation generated by the generating unit. For example, the analyzing unit analyzes the user's pronunciation and points out any differences from the correct pronunciation. The analyzing unit can also analyze the user's pronunciation and evaluate the accuracy of the pronunciation. The providing unit provides feedback based on the results of the analysis by the analyzing unit. The providing unit, for example, evaluates the accuracy of the user's pronunciation and points out areas for improvement. The providing unit can also provide feedback according to the user's level of proficiency. For example, the providing unit provides basic pronunciation instruction to beginners, intermediate pronunciation instruction to intermediate learners, and native pronunciation instruction to advanced learners. In this way, the pronunciation correction system according to the embodiment generates pronunciation that is intermediate between the user's native language and the language they wish to learn, allowing them to gradually learn native pronunciation.

[0030] The generation unit can generate an intermediate pronunciation based on audio data of the native language and the language to be learned. The generation unit generates an intermediate pronunciation based on audio data of the native language and the language to be learned, for example. The generation unit analyzes audio data of Japanese and English, for example, and generates an intermediate pronunciation. The generation unit can also use a generation AI to generate an intermediate pronunciation based on audio data of the native language and the language to be learned. For example, the generation AI analyzes audio data of Japanese and English and generates an intermediate pronunciation. In this way, by generating an intermediate pronunciation based on audio data of the native language and the language to be learned, more accurate intermediate pronunciation can be provided.

[0031] The analysis unit can analyze the user's pronunciation and point out any differences from the correct pronunciation. For example, the analysis unit analyzes the user's pronunciation and points out any differences from the correct pronunciation. For example, the analysis unit analyzes the voice pronounced by the user and points out any differences from the correct pronunciation. The analysis unit can also analyze the user's pronunciation and evaluate the accuracy of the pronunciation. For example, the analysis unit evaluates the accuracy of the user's pronunciation and points out areas for improvement. In this way, by analyzing the user's pronunciation and pointing out any differences from the correct pronunciation, the user can correct their pronunciation.

[0032] The providing unit can provide feedback according to the user's level of proficiency. The providing unit provides feedback according to the user's level of proficiency, for example. The providing unit, for example, provides basic pronunciation instruction to beginners, intermediate pronunciation instruction to intermediate learners, and native pronunciation instruction to advanced learners. The providing unit can also evaluate the accuracy of the user's pronunciation and point out areas for improvement. For example, the providing unit evaluates the accuracy of the user's pronunciation and points out areas for improvement. In this way, by providing feedback according to the user's level of proficiency, the user can effectively learn pronunciation.

[0033] The generation unit can change the middle pronunciation in stages according to the user's proficiency. The generation unit, for example, changes the middle pronunciation in stages according to the user's proficiency. For example, the generation unit provides a pronunciation close to the native language for a beginner, a middle pronunciation for an intermediate learner, and a pronunciation close to native pronunciation for an advanced learner. The generation unit can also change the middle pronunciation in stages according to the user's proficiency using a generation AI. For example, the generation AI changes the middle pronunciation according to the user's proficiency. This allows the user to correct their pronunciation naturally by changing the middle pronunciation in stages according to the user's proficiency.

[0034] The providing unit can record the progress of the user's pronunciation practice and provide appropriate feedback. The providing unit, for example, records the progress of the user's pronunciation practice and provides appropriate feedback. The providing unit, for example, records the progress of the user's pronunciation practice and provides appropriate feedback. The providing unit can also record the progress of the user's pronunciation practice and provide appropriate feedback. For example, the providing unit records the progress of the user's pronunciation practice and provides appropriate feedback. In this way, by recording the progress of the user's pronunciation practice and providing appropriate feedback, the user can effectively learn pronunciation.

[0035] The reception unit can analyze the user's past learning history and select the optimal language information reception method. The reception unit, for example, analyzes the user's past learning history and selects the optimal language information reception method. The reception unit, for example, preferentially suggests a language information reception method that the user has frequently used in the past. The reception unit can also select the most effective reception method from the user's learning history. The reception unit can also suggest the optimal reception method for a specific time period based on the user's learning history. For example, the reception unit preferentially suggests a language information reception method that the user has frequently used in the past. In this way, the optimal reception method is provided by analyzing the user's past learning history, thereby improving the learning effect.

[0036] The reception unit can perform filtering based on the user's current learning situation and areas of interest when receiving the language information. For example, the reception unit performs filtering based on the user's current learning situation and areas of interest when receiving the language information. For example, the reception unit only accepts information related to the language the user is currently learning. The reception unit can also preferentially accept language information related to the user's areas of interest. The reception unit can also filter and accept language information of an appropriate level of difficulty according to the user's learning progress. For example, the reception unit only accepts information related to the language the user is currently learning. In this way, filtering information based on the user's learning situation and areas of interest supports more effective learning.

[0037] The reception unit can select the optimum reception means according to the user's input method when receiving language information. For example, the reception unit selects the optimum reception means according to the user's input method (voice, text, image, etc.) when receiving language information. For example, if the user desires voice input, the reception unit may preferentially accept voice input. Furthermore, if the user desires text input, the reception unit may preferentially accept text input. Furthermore, if the user desires image input, the reception unit may preferentially accept image input. For example, if the user desires voice input, the reception unit preferentially accepts voice input. In this way, convenience for the user is improved by selecting the optimum reception means according to the user's input method.

[0038] The reception unit can preferentially accept highly relevant language information in consideration of the user's geographical location information when receiving language information. For example, the reception unit preferentially accepts highly relevant language information in consideration of the user's geographical location information when receiving language information. For example, the reception unit preferentially accepts language information for the area where the user is currently located. The reception unit can also preferentially accept language information for a destination where the user is traveling. The reception unit can also preferentially accept language information for the area where the user lives. For example, the reception unit preferentially accepts language information for the area where the user is currently located. In this way, highly relevant information is provided by taking the user's geographical location information into consideration, thereby improving learning effectiveness.

[0039] The reception unit can analyze the user's social media activities and receive related language information when receiving the language information. For example, the reception unit analyzes the user's social media activities and receives related language information when receiving the language information. For example, the reception unit preferentially receives language information in which the user has shown interest on social media. The reception unit can also analyze the content of the user's posts on social media and receive related language information. The reception unit can also receive related language information by referring to the activities of the user's friends on social media. For example, the reception unit preferentially receives language information in which the user has shown interest on social media. In this way, by analyzing the user's social media activities, highly relevant information can be provided, improving learning effectiveness.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving linguistic information. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving linguistic information. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also improve the reception method by reflecting the user's feedback. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. In this way, the optimal reception method is provided by reflecting the user's past feedback, thereby improving the learning effect.

[0041] The generation unit can adjust the level of detail of the generation based on the importance of the audio data of the language when generating the intermediate pronunciation. For example, the generation unit adjusts the level of detail of the generation based on the importance of the audio data of the language when generating the intermediate pronunciation. For example, the generation unit generates a detailed intermediate pronunciation based on important audio data. The generation unit can also generate a simplified intermediate pronunciation based on audio data with low importance. The generation unit can also adjust the level of detail of the generation according to the importance of the audio data of the language. For example, the generation unit generates a detailed intermediate pronunciation based on important audio data. In this way, adjusting the level of detail of the generation based on the importance of the audio data of the language provides more effective pronunciation practice.

[0042] The generation unit may apply different generation algorithms depending on the language category when generating a mid pronunciation. For example, the generation unit may apply different generation algorithms depending on the language category when generating a mid pronunciation. For example, the generation unit may apply a specific generation algorithm to a Romance language. The generation unit may also apply a different generation algorithm to a Germanic language. The generation unit may also apply a different generation algorithm to an Asian language. For example, the generation unit may apply a specific generation algorithm to a Romance language. In this way, applying different generation algorithms depending on the language category provides more effective pronunciation practice.

[0043] The generation unit can improve the accuracy of generation when generating an intermediate pronunciation by referring to the user's past pronunciation data. For example, the generation unit improves the accuracy of generation when generating an intermediate pronunciation by referring to the user's past pronunciation data. The generation unit generates an optimal intermediate pronunciation, for example, based on the user's past pronunciation data. The generation unit can also analyze the user's pronunciation data and improve the accuracy of generation. The generation unit can also improve the accuracy of generation by referring to the user's past pronunciation data. For example, the generation unit generates an optimal intermediate pronunciation based on the user's past pronunciation data. In this way, by referring to the user's past pronunciation data, the accuracy of generation is improved and more effective pronunciation practice is provided.

[0044] The generation unit can determine the priority of generation when generating intermediate pronunciations based on the submission time of the language. For example, the generation unit determines the priority of generation when generating intermediate pronunciations based on the submission time of the language. For example, the generation unit preferentially generates intermediate pronunciations for languages ​​with upcoming submission deadlines. The generation unit can also postpone the generation of intermediate pronunciations for languages ​​with more distant submission deadlines. The generation unit can also determine the priority of generation based on the submission time of the language. For example, the generation unit preferentially generates intermediate pronunciations for languages ​​with upcoming submission deadlines. In this way, efficient pronunciation practice is provided by determining the priority of generation based on the submission time of the language.

[0045] The generation unit can adjust the order of generation based on the relevance of the languages ​​when generating mid-pronunciations. For example, the generation unit adjusts the order of generation based on the relevance of the languages ​​when generating mid-pronunciations. For example, the generation unit preferentially generates mid-pronunciations of highly related languages. The generation unit can also postpone the generation of mid-pronunciations of less related languages. The generation unit can also adjust the order of generation based on the relevance of the languages. For example, the generation unit preferentially generates mid-pronunciations of highly related languages. In this way, adjusting the order of generation based on the relevance of the languages ​​provides efficient pronunciation practice.

[0046] The generation unit can adjust the use of technical terms in the generation of intermediate pronunciations in accordance with the user's level of expertise when generating intermediate pronunciations. For example, the generation unit adjusts the use of technical terms in the generation of intermediate pronunciations in accordance with the user's level of expertise when generating intermediate pronunciations. For example, the generation unit generates intermediate pronunciations that make heavy use of technical terms for users with high levels of expertise. The generation unit can also generate intermediate pronunciations that use simple terms for users with low levels of expertise. The generation unit can also adjust the use of technical terms in the generation of intermediate pronunciations in accordance with the user's level of expertise. For example, the generation unit generates intermediate pronunciations that make heavy use of technical terms for users with high levels of expertise. In this way, adjusting the use of technical terms in accordance with the user's level of expertise provides more effective pronunciation practice.

[0047] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's pronunciations during pronunciation analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between the user's pronunciations during pronunciation analysis. For example, the analysis unit analyzes the interrelationships between the user's pronunciations and improves the accuracy of the analysis. The analysis unit can also perform optimal pronunciation analysis by taking into account the interrelationships between the user's pronunciations. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between the user's pronunciations. For example, the analysis unit analyzes the interrelationships between the user's pronunciations and improves the accuracy of the analysis. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between the user's pronunciations.

[0048] The analysis unit can perform pronunciation analysis taking into account user attribute information. For example, the analysis unit performs pronunciation analysis taking into account user attribute information. For example, the analysis unit performs pronunciation analysis taking into account the user's age and gender. The analysis unit can also perform pronunciation analysis taking into account the user's learning history. The analysis unit can also perform optimal pronunciation analysis based on the user's attribute information. For example, the analysis unit performs pronunciation analysis taking into account the user's age and gender. In this way, more effective pronunciation analysis can be provided by taking into account the user's attribute information.

[0049] The analysis unit can weight the analysis based on the user's pronunciation frequency during pronunciation analysis. For example, the analysis unit weights the analysis based on the user's pronunciation frequency during pronunciation analysis. For example, the analysis unit weights words that the user frequently pronounces. The analysis unit can also reduce the weighting of words that the user does not pronounce often. The analysis unit can also adjust the weighting of the analysis based on the user's pronunciation frequency. For example, the analysis unit weights words that the user frequently pronounces. In this way, weighting the analysis based on the user's pronunciation frequency provides more effective pronunciation analysis.

[0050] The analysis unit can perform pronunciation analysis taking into account the geographical distribution of users. For example, the analysis unit performs pronunciation analysis taking into account the geographical distribution of users. For example, the analysis unit performs analysis taking into account the pronunciation characteristics of the area where the user lives. The analysis unit can also perform analysis taking into account the pronunciation characteristics of areas to which the user travels. The analysis unit can also perform optimal pronunciation analysis based on the geographical distribution of users. For example, the analysis unit performs analysis taking into account the pronunciation characteristics of the area where the user lives. In this way, more effective pronunciation analysis can be provided by taking into account the geographical distribution of users.

[0051] The analysis unit can improve the accuracy of the analysis by referring to related literature during pronunciation analysis. For example, the analysis unit improves the accuracy of the analysis by referring to related literature during pronunciation analysis. For example, the analysis unit improves the accuracy of the pronunciation analysis by referring to related literature. The analysis unit can also perform optimal pronunciation analysis based on data from related literature. The analysis unit can also improve the accuracy of the pronunciation analysis by referring to related literature. For example, the analysis unit improves the accuracy of the pronunciation analysis by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature.

[0052] The analysis unit can perform pronunciation analysis taking into account the market value of the user. For example, the analysis unit performs pronunciation analysis taking into account the market value of the user. For example, the analysis unit performs pronunciation analysis based on the user's occupation or industry. The analysis unit can also improve the accuracy of the pronunciation analysis by taking into account the user's market value. The analysis unit can also perform optimal pronunciation analysis based on the user's market value. For example, the analysis unit performs pronunciation analysis based on the user's occupation or industry. In this way, more effective pronunciation analysis can be provided by taking into account the user's market value.

[0053] The providing unit can select the optimal providing method by referring to the user's past feedback history when providing feedback. For example, the providing unit selects the optimal providing method by referring to the user's past feedback history when providing feedback. The providing unit selects the optimal providing method, for example, based on the user's past feedback history. The providing unit can also preferentially suggest a specific providing method from the user's feedback history. The providing unit can also improve the providing method by referring to the user's feedback history. For example, the providing unit selects the optimal providing method based on the user's past feedback history. In this way, by referring to the user's past feedback history, the optimal providing method is provided and the learning effect is enhanced.

[0054] The providing unit can customize the content to be provided based on the user's current learning situation when providing feedback. For example, the providing unit customizes the content to be provided based on the user's current learning situation when providing feedback. For example, the providing unit provides optimal feedback taking into account the user's current learning situation. The providing unit can also customize the feedback content according to the user's learning progress. The providing unit can also provide appropriate feedback based on the user's learning situation. For example, the providing unit provides optimal feedback taking into account the user's current learning situation. In this way, more effective feedback can be provided by customizing the content to be provided based on the user's current learning situation.

[0055] The providing unit can improve the providing method by reflecting the user's feedback when providing feedback. For example, the providing unit improves the providing method by reflecting the user's feedback when providing feedback. The providing unit improves the providing method, for example, based on the user's feedback. The providing unit can also suggest an optimal providing method based on the user's feedback. The providing unit can also customize the providing method by reflecting the user's feedback. For example, the providing unit improves the providing method based on the user's feedback. In this way, the providing method is improved by reflecting the user's feedback, and the learning effect is enhanced.

[0056] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing feedback. For example, the providing unit selects the optimal providing method by taking into consideration the user's geographical location information when providing feedback. For example, the providing unit provides optimal feedback by taking into consideration the characteristics of the area where the user is currently located. The providing unit can also provide optimal feedback by taking into consideration the characteristics of the area where the user is traveling. The providing unit can also provide optimal feedback based on the user's geographical location information. For example, the providing unit provides optimal feedback by taking into consideration the characteristics of the area where the user is currently located. In this way, the optimal providing method is provided by taking into consideration the user's geographical location information, thereby improving the learning effect.

[0057] The providing unit can analyze the user's social media activity and suggest a means of providing feedback when providing feedback. For example, when providing feedback, the providing unit analyzes the user's social media activity and suggests a means of providing feedback. For example, the providing unit analyzes the user's social media activity and suggests an optimal means of providing feedback. The providing unit can also suggest an appropriate means of providing feedback based on the content of the user's social media posts. The providing unit can also suggest an optimal means of providing feedback by taking into account the activities of the user's friends on social media. For example, the providing unit analyzes the user's social media activity and suggests an optimal means of providing feedback. In this way, by analyzing the user's social media activity, an optimal means of providing feedback is provided, thereby improving learning effectiveness.

[0058] The providing unit can customize the provision method by reflecting the user's past feedback when providing feedback. For example, the providing unit customizes the provision method by reflecting the user's past feedback when providing feedback. For example, the providing unit suggests an optimal provision method based on the user's past feedback. The providing unit can also preferentially suggest a specific provision method based on the user's past feedback. The providing unit can also improve the provision method by reflecting the user's feedback. For example, the providing unit suggests an optimal provision method based on the user's past feedback. In this way, the optimal provision method is provided by reflecting the user's past feedback, thereby improving the learning effect.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can propose an optimal study schedule based on the user's study history. For example, the reception unit can analyze the time periods and frequency at which the user studied in the past and propose an optimal study schedule. The reception unit can also dynamically adjust the schedule according to the user's study progress. Furthermore, the reception unit can also propose a study schedule that matches the user's lifestyle. This allows the user to continue studying without strain and effectively correct their pronunciation.

[0061] The generation unit can generate an individually customized pronunciation practice plan based on the user's pronunciation data. For example, the generation unit can identify the user's weak points in pronunciation and generate a practice plan to overcome those weak points. The generation unit can also generate a practice plan that makes use of the user's strong points in pronunciation. Furthermore, the generation unit can generate a practice plan that matches the user's learning style. This allows the user to improve their pronunciation with a practice plan that is optimal for them.

[0062] The analyzer can analyze the user's pronunciation data in real time and provide immediate feedback. For example, the analyzer can analyze the user's pronunciation data immediately after the user pronounces a word and provide immediate feedback. The analyzer can also evaluate the accuracy of the user's pronunciation in real time and point out areas for improvement. Furthermore, the analyzer can track the user's pronunciation progress in real time and provide appropriate feedback. This allows the user to immediately correct their pronunciation and effectively correct their pronunciation.

[0063] The providing unit can introduce gamification elements to increase the user's motivation for pronunciation practice. For example, the providing unit can award points or badges according to the user's progress in pronunciation practice. The providing unit can also provide rewards according to the goals the user achieves. Furthermore, the providing unit can provide a ranking function that allows users to compete with each other. This allows the user to continue pronunciation practice while having fun, and can effectively correct their pronunciation.

[0064] The providing unit can provide a dashboard function to visualize the progress of the user's pronunciation practice. For example, the providing unit can display the progress of the user's pronunciation practice in the form of a graph or chart. The providing unit can also visually display the accuracy of the user's pronunciation and areas for improvement. Furthermore, the providing unit can display the user's pronunciation practice history, allowing the user to review past practice results. This allows the user to understand the progress of their pronunciation practice and effectively correct their pronunciation.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives information about the user's native language and the language they wish to learn. The user simply selects their native language and the language they wish to learn. Step 2: The generation unit uses a generation AI to generate a pronunciation that is intermediate between the native language and the language to be learned based on the information received by the reception unit. The generation AI generates the intermediate pronunciation using, for example, a text generation AI (e.g., LLM). The generation unit can also generate the intermediate pronunciation based on audio data of the native language and the language to be learned. For example, the generation AI analyzes audio data of Japanese and English and generates an intermediate pronunciation. Step 3: The analyzer analyzes the user's pronunciation based on the intermediate pronunciation generated by the generator. The analyzer analyzes the user's pronunciation and identifies any discrepancies from the correct pronunciation. The analyzer can also analyze the user's pronunciation and evaluate the accuracy of the pronunciation. Step 4: The providing unit provides feedback based on the results of the analysis by the analyzing unit. The providing unit evaluates the accuracy of the user's pronunciation and points out areas for improvement. The providing unit can also provide feedback according to the user's level of proficiency. For example, the providing unit may provide basic pronunciation instruction to beginners, intermediate pronunciation instruction to intermediate learners, and native pronunciation instruction to advanced learners.

[0067] (Example 2) A pronunciation correction system according to an embodiment of the present invention generates a pronunciation intermediate between a user's native language and a language to be learned, and gradually teaches native pronunciation. The pronunciation correction system accepts information about the user's native language and the language to be learned, generates the intermediate pronunciation using a generation AI (e.g., LLM), analyzes the user's pronunciation, and provides feedback. For example, the pronunciation correction system inputs the user's native language and the language to be learned. For example, the user simply selects their native language and the language to be learned. Next, the pronunciation correction system uses the generation AI to generate the intermediate pronunciation based on audio data of the native language and the language to be learned. For example, if the generation AI is to generate a pronunciation intermediate between Japanese and English, it analyzes Japanese audio data and English audio data and generates the intermediate pronunciation. Next, the pronunciation correction system gradually changes the generated intermediate pronunciation according to the user's proficiency level. For example, beginners are provided with a pronunciation closer to their native language, intermediate pronunciations are provided to intermediate learners, and pronunciation closer to native pronunciations is provided to advanced learners. Next, the pronunciation correction system analyzes the user's pronunciation and provides feedback as the user practices pronunciation. For example, by analyzing the user's pronunciation and pointing out any discrepancies from the correct pronunciation, the user can correct their pronunciation. This allows the pronunciation correction system to smoothly correct the user's pronunciation and acquire native pronunciation. This allows the pronunciation correction system to smoothly correct the user's pronunciation and acquire native pronunciation. For example, if a user whose native language is Japanese is learning English, by gradually learning intermediate pronunciation, the user can eventually acquire native English pronunciation. This allows the user to effectively learn pronunciation without having to give up on pronunciation practice.

[0068] A pronunciation correction system according to an embodiment includes a receiving unit, a generating unit, an analyzing unit, and a providing unit. The receiving unit receives information about a user's native language and a language to learn. The user simply selects, for example, their native language and the language to learn. The generating unit uses a generation AI to generate a pronunciation that is intermediate between the native language and the language to learn based on the information received by the receiving unit. The generation AI generates the intermediate pronunciation using, for example, a text generation AI (e.g., LLM). The generating unit can also generate the intermediate pronunciation based on audio data of the native language and the language to learn. For example, the generation AI analyzes audio data of Japanese and English and generates an intermediate pronunciation. The analyzing unit analyzes the user's pronunciation based on the intermediate pronunciation generated by the generating unit. For example, the analyzing unit analyzes the user's pronunciation and points out any differences from the correct pronunciation. The analyzing unit can also analyze the user's pronunciation and evaluate the accuracy of the pronunciation. The providing unit provides feedback based on the results of the analysis by the analyzing unit. The providing unit, for example, evaluates the accuracy of the user's pronunciation and points out areas for improvement. The providing unit can also provide feedback according to the user's level of proficiency. For example, the providing unit provides basic pronunciation instruction to beginners, intermediate pronunciation instruction to intermediate learners, and native pronunciation instruction to advanced learners. In this way, the pronunciation correction system according to the embodiment generates pronunciation that is intermediate between the user's native language and the language they wish to learn, allowing them to gradually learn native pronunciation.

[0069] The generation unit can generate an intermediate pronunciation based on audio data of the native language and the language to be learned. The generation unit generates an intermediate pronunciation based on audio data of the native language and the language to be learned, for example. The generation unit analyzes audio data of Japanese and English, for example, and generates an intermediate pronunciation. The generation unit can also use a generation AI to generate an intermediate pronunciation based on audio data of the native language and the language to be learned. For example, the generation AI analyzes audio data of Japanese and English and generates an intermediate pronunciation. In this way, by generating an intermediate pronunciation based on audio data of the native language and the language to be learned, more accurate intermediate pronunciation can be provided.

[0070] The analysis unit can analyze the user's pronunciation and point out any differences from the correct pronunciation. For example, the analysis unit analyzes the user's pronunciation and points out any differences from the correct pronunciation. For example, the analysis unit analyzes the voice pronounced by the user and points out any differences from the correct pronunciation. The analysis unit can also analyze the user's pronunciation and evaluate the accuracy of the pronunciation. For example, the analysis unit evaluates the accuracy of the user's pronunciation and points out areas for improvement. In this way, by analyzing the user's pronunciation and pointing out any differences from the correct pronunciation, the user can correct their pronunciation.

[0071] The providing unit can provide feedback according to the user's level of proficiency. The providing unit provides feedback according to the user's level of proficiency, for example. The providing unit, for example, provides basic pronunciation instruction to beginners, intermediate pronunciation instruction to intermediate learners, and native pronunciation instruction to advanced learners. The providing unit can also evaluate the accuracy of the user's pronunciation and point out areas for improvement. For example, the providing unit evaluates the accuracy of the user's pronunciation and points out areas for improvement. In this way, by providing feedback according to the user's level of proficiency, the user can effectively learn pronunciation.

[0072] The generation unit can change the middle pronunciation in stages according to the user's proficiency. The generation unit, for example, changes the middle pronunciation in stages according to the user's proficiency. For example, the generation unit provides a pronunciation close to the native language for a beginner, a middle pronunciation for an intermediate learner, and a pronunciation close to native pronunciation for an advanced learner. The generation unit can also change the middle pronunciation in stages according to the user's proficiency using a generation AI. For example, the generation AI changes the middle pronunciation according to the user's proficiency. This allows the user to correct their pronunciation naturally by changing the middle pronunciation in stages according to the user's proficiency.

[0073] The providing unit can record the progress of the user's pronunciation practice and provide appropriate feedback. The providing unit, for example, records the progress of the user's pronunciation practice and provides appropriate feedback. The providing unit, for example, records the progress of the user's pronunciation practice and provides appropriate feedback. The providing unit can also record the progress of the user's pronunciation practice and provide appropriate feedback. For example, the providing unit records the progress of the user's pronunciation practice and provides appropriate feedback. In this way, by recording the progress of the user's pronunciation practice and providing appropriate feedback, the user can effectively learn pronunciation.

[0074] The reception unit can estimate the user's emotions and adjust the timing for accepting information about the native language and the language to be learned based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the timing for accepting information about the native language and the language to be learned based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit accepts information about the native language and the language to be learned at a time when the user is able to relax. Furthermore, if the user is concentrating, the reception unit can immediately accept information about the native language and the language to be learned. Furthermore, if the user is tired, the reception unit can accept information about the native language and the language to be learned after a break. For example, if the user is feeling stressed, the reception unit accepts information about the native language and the language to be learned at a time when the user is able to relax. In this way, adjusting the timing for accepting information according to the user's emotions reduces the user's stress and promotes effective learning. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can analyze the user's past learning history and select the optimal language information reception method. The reception unit, for example, analyzes the user's past learning history and selects the optimal language information reception method. The reception unit, for example, preferentially suggests a language information reception method that the user has frequently used in the past. The reception unit can also select the most effective reception method from the user's learning history. The reception unit can also suggest the optimal reception method for a specific time period based on the user's learning history. For example, the reception unit preferentially suggests a language information reception method that the user has frequently used in the past. In this way, the optimal reception method is provided by analyzing the user's past learning history, thereby improving the learning effect.

[0076] The reception unit can perform filtering based on the user's current learning situation and areas of interest when receiving the language information. For example, the reception unit performs filtering based on the user's current learning situation and areas of interest when receiving the language information. For example, the reception unit only accepts information related to the language the user is currently learning. The reception unit can also preferentially accept language information related to the user's areas of interest. The reception unit can also filter and accept language information of an appropriate level of difficulty according to the user's learning progress. For example, the reception unit only accepts information related to the language the user is currently learning. In this way, filtering information based on the user's learning situation and areas of interest supports more effective learning.

[0077] The reception unit can select the optimum reception means according to the user's input method when receiving language information. For example, the reception unit selects the optimum reception means according to the user's input method (voice, text, image, etc.) when receiving language information. For example, if the user desires voice input, the reception unit may preferentially accept voice input. Furthermore, if the user desires text input, the reception unit may preferentially accept text input. Furthermore, if the user desires image input, the reception unit may preferentially accept image input. For example, if the user desires voice input, the reception unit preferentially accepts voice input. In this way, convenience for the user is improved by selecting the optimum reception means according to the user's input method.

[0078] The reception unit can estimate the user's emotion and determine the priority of the linguistic information to be accepted based on the estimated user's emotion. For example, the reception unit estimates the user's emotion and determines the priority of the linguistic information to be accepted based on the estimated user's emotion. For example, when the user is relaxed, the reception unit can preferentially accept difficult linguistic information. Furthermore, when the user is stressed, the reception unit can preferentially accept simple linguistic information. Furthermore, when the user is concentrating, the reception unit can preferentially accept important linguistic information. For example, when the user is relaxed, the reception unit preferentially accepts difficult linguistic information. This improves learning efficiency by determining the priority of information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can preferentially accept highly relevant language information in consideration of the user's geographical location information when receiving language information. For example, the reception unit preferentially accepts highly relevant language information in consideration of the user's geographical location information when receiving language information. For example, the reception unit preferentially accepts language information for the area where the user is currently located. The reception unit can also preferentially accept language information for a destination where the user is traveling. The reception unit can also preferentially accept language information for the area where the user lives. For example, the reception unit preferentially accepts language information for the area where the user is currently located. In this way, highly relevant information is provided by taking the user's geographical location information into consideration, thereby improving learning effectiveness.

[0080] The reception unit can analyze the user's social media activities and receive related language information when receiving the language information. For example, the reception unit analyzes the user's social media activities and receives related language information when receiving the language information. For example, the reception unit preferentially receives language information in which the user has shown interest on social media. The reception unit can also analyze the content of the user's posts on social media and receive related language information. The reception unit can also receive related language information by referring to the activities of the user's friends on social media. For example, the reception unit preferentially receives language information in which the user has shown interest on social media. In this way, by analyzing the user's social media activities, highly relevant information can be provided, improving learning effectiveness.

[0081] The reception unit can customize the reception method by reflecting the user's past feedback when receiving linguistic information. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving linguistic information. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also improve the reception method by reflecting the user's feedback. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. In this way, the optimal reception method is provided by reflecting the user's past feedback, thereby improving the learning effect.

[0082] The generation unit can estimate the user's emotion and adjust the method for generating the mid-pronunciation based on the estimated user's emotion. For example, the generation unit estimates the user's emotion and adjusts the method for generating the mid-pronunciation based on the estimated user's emotion. For example, if the user is relaxed, the generation unit generates a mid-pronunciation that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a mid-pronunciation that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a mid-pronunciation that adds a visually stimulating effect. For example, if the user is relaxed, the generation unit generates a mid-pronunciation that progresses at a leisurely pace. This adjusts the method for generating the mid-pronunciation according to the user's emotion, thereby providing more effective pronunciation practice. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The generation unit can adjust the level of detail of the generation based on the importance of the audio data of the language when generating the intermediate pronunciation. For example, the generation unit adjusts the level of detail of the generation based on the importance of the audio data of the language when generating the intermediate pronunciation. For example, the generation unit generates a detailed intermediate pronunciation based on important audio data. The generation unit can also generate a simplified intermediate pronunciation based on audio data with low importance. The generation unit can also adjust the level of detail of the generation according to the importance of the audio data of the language. For example, the generation unit generates a detailed intermediate pronunciation based on important audio data. In this way, adjusting the level of detail of the generation based on the importance of the audio data of the language provides more effective pronunciation practice.

[0084] The generation unit may apply different generation algorithms depending on the language category when generating a mid pronunciation. For example, the generation unit may apply different generation algorithms depending on the language category when generating a mid pronunciation. For example, the generation unit may apply a specific generation algorithm to a Romance language. The generation unit may also apply a different generation algorithm to a Germanic language. The generation unit may also apply a different generation algorithm to an Asian language. For example, the generation unit may apply a specific generation algorithm to a Romance language. In this way, applying different generation algorithms depending on the language category provides more effective pronunciation practice.

[0085] The generation unit can improve the accuracy of generation when generating an intermediate pronunciation by referring to the user's past pronunciation data. For example, the generation unit improves the accuracy of generation when generating an intermediate pronunciation by referring to the user's past pronunciation data. The generation unit generates an optimal intermediate pronunciation, for example, based on the user's past pronunciation data. The generation unit can also analyze the user's pronunciation data and improve the accuracy of generation. The generation unit can also improve the accuracy of generation by referring to the user's past pronunciation data. For example, the generation unit generates an optimal intermediate pronunciation based on the user's past pronunciation data. In this way, by referring to the user's past pronunciation data, the accuracy of generation is improved and more effective pronunciation practice is provided.

[0086] The generation unit can estimate the user's emotion and adjust the length of the mid-tone based on the estimated emotion. For example, the generation unit estimates the user's emotion and adjusts the length of the mid-tone based on the estimated emotion. For example, the generation unit generates a longer mid-tone when the user is relaxed. The generation unit can also generate a shorter mid-tone when the user is in a hurry. The generation unit can also generate a mid-tone with a visually stimulating effect when the user is excited. For example, the generation unit generates a longer mid-tone when the user is relaxed. This provides more effective pronunciation practice by adjusting the length of the mid-tone according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The generation unit can determine the priority of generation when generating intermediate pronunciations based on the submission time of the language. For example, the generation unit determines the priority of generation when generating intermediate pronunciations based on the submission time of the language. For example, the generation unit preferentially generates intermediate pronunciations for languages ​​with upcoming submission deadlines. The generation unit can also postpone the generation of intermediate pronunciations for languages ​​with more distant submission deadlines. The generation unit can also determine the priority of generation based on the submission time of the language. For example, the generation unit preferentially generates intermediate pronunciations for languages ​​with upcoming submission deadlines. In this way, efficient pronunciation practice is provided by determining the priority of generation based on the submission time of the language.

[0088] The generation unit can adjust the order of generation based on the relevance of the languages ​​when generating mid-pronunciations. For example, the generation unit adjusts the order of generation based on the relevance of the languages ​​when generating mid-pronunciations. For example, the generation unit preferentially generates mid-pronunciations of highly related languages. The generation unit can also postpone the generation of mid-pronunciations of less related languages. The generation unit can also adjust the order of generation based on the relevance of the languages. For example, the generation unit preferentially generates mid-pronunciations of highly related languages. In this way, adjusting the order of generation based on the relevance of the languages ​​provides efficient pronunciation practice.

[0089] The generation unit can adjust the use of technical terms in the generation of intermediate pronunciations in accordance with the user's level of expertise when generating intermediate pronunciations. For example, the generation unit adjusts the use of technical terms in the generation of intermediate pronunciations in accordance with the user's level of expertise when generating intermediate pronunciations. For example, the generation unit generates intermediate pronunciations that make heavy use of technical terms for users with high levels of expertise. The generation unit can also generate intermediate pronunciations that use simple terms for users with low levels of expertise. The generation unit can also adjust the use of technical terms in the generation of intermediate pronunciations in accordance with the user's level of expertise. For example, the generation unit generates intermediate pronunciations that make heavy use of technical terms for users with high levels of expertise. In this way, adjusting the use of technical terms in accordance with the user's level of expertise provides more effective pronunciation practice.

[0090] The analysis unit can estimate the user's emotions and adjust the standards for pronunciation analysis based on the estimated user's emotions. For example, the analysis unit estimates the user's emotions and adjusts the standards for pronunciation analysis based on the estimated user's emotions. For example, the analysis unit performs detailed pronunciation analysis when the user is relaxed. The analysis unit can also perform simplified pronunciation analysis when the user is in a hurry. The analysis unit can also perform pronunciation analysis with visually stimulating effects when the user is excited. For example, the analysis unit performs detailed pronunciation analysis when the user is relaxed. This adjusts the standards for pronunciation analysis according to the user's emotions, thereby providing more effective pronunciation analysis. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's pronunciations during pronunciation analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between the user's pronunciations during pronunciation analysis. For example, the analysis unit analyzes the interrelationships between the user's pronunciations and improves the accuracy of the analysis. The analysis unit can also perform optimal pronunciation analysis by taking into account the interrelationships between the user's pronunciations. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between the user's pronunciations. For example, the analysis unit analyzes the interrelationships between the user's pronunciations and improves the accuracy of the analysis. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between the user's pronunciations.

[0092] The analysis unit can perform pronunciation analysis taking into account user attribute information. For example, the analysis unit performs pronunciation analysis taking into account user attribute information. For example, the analysis unit performs pronunciation analysis taking into account the user's age and gender. The analysis unit can also perform pronunciation analysis taking into account the user's learning history. The analysis unit can also perform optimal pronunciation analysis based on the user's attribute information. For example, the analysis unit performs pronunciation analysis taking into account the user's age and gender. In this way, more effective pronunciation analysis can be provided by taking into account the user's attribute information.

[0093] The analysis unit can weight the analysis based on the user's pronunciation frequency during pronunciation analysis. For example, the analysis unit weights the analysis based on the user's pronunciation frequency during pronunciation analysis. For example, the analysis unit weights words that the user frequently pronounces. The analysis unit can also reduce the weighting of words that the user does not pronounce often. The analysis unit can also adjust the weighting of the analysis based on the user's pronunciation frequency. For example, the analysis unit weights words that the user frequently pronounces. In this way, weighting the analysis based on the user's pronunciation frequency provides more effective pronunciation analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. Furthermore, when the user is excited, the analysis unit can prioritize displaying visually stimulating analysis results. For example, when the user is relaxed, the analysis unit prioritizes displaying detailed analysis results. In this way, by adjusting the display order of the analysis results according to the user's emotions, more effective feedback is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The analysis unit can perform pronunciation analysis taking into account the geographical distribution of users. For example, the analysis unit performs pronunciation analysis taking into account the geographical distribution of users. For example, the analysis unit performs analysis taking into account the pronunciation characteristics of the area where the user lives. The analysis unit can also perform analysis taking into account the pronunciation characteristics of areas to which the user travels. The analysis unit can also perform optimal pronunciation analysis based on the geographical distribution of users. For example, the analysis unit performs analysis taking into account the pronunciation characteristics of the area where the user lives. In this way, more effective pronunciation analysis can be provided by taking into account the geographical distribution of users.

[0096] The analysis unit can improve the accuracy of the analysis by referring to related literature during pronunciation analysis. For example, the analysis unit improves the accuracy of the analysis by referring to related literature during pronunciation analysis. For example, the analysis unit improves the accuracy of the pronunciation analysis by referring to related literature. The analysis unit can also perform optimal pronunciation analysis based on data from related literature. The analysis unit can also improve the accuracy of the pronunciation analysis by referring to related literature. For example, the analysis unit improves the accuracy of the pronunciation analysis by referring to related literature. In this way, the accuracy of the analysis is improved by referring to related literature.

[0097] The analysis unit can perform pronunciation analysis taking into account the market value of the user. For example, the analysis unit performs pronunciation analysis taking into account the market value of the user. For example, the analysis unit performs pronunciation analysis based on the user's occupation or industry. The analysis unit can also improve the accuracy of the pronunciation analysis by taking into account the user's market value. The analysis unit can also perform optimal pronunciation analysis based on the user's market value. For example, the analysis unit performs pronunciation analysis based on the user's occupation or industry. In this way, more effective pronunciation analysis can be provided by taking into account the user's market value.

[0098] The providing unit can estimate the user's emotion and adjust the feedback providing method based on the estimated user's emotion. For example, the providing unit estimates the user's emotion and adjusts the feedback providing method based on the estimated user's emotion. For example, the providing unit provides detailed feedback when the user is relaxed. Furthermore, the providing unit can provide brief feedback when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating feedback when the user is excited. For example, the providing unit provides detailed feedback when the user is relaxed. In this way, more effective feedback is provided by adjusting the feedback providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The providing unit can select the optimal providing method by referring to the user's past feedback history when providing feedback. For example, the providing unit selects the optimal providing method by referring to the user's past feedback history when providing feedback. The providing unit selects the optimal providing method, for example, based on the user's past feedback history. The providing unit can also preferentially suggest a specific providing method from the user's feedback history. The providing unit can also improve the providing method by referring to the user's feedback history. For example, the providing unit selects the optimal providing method based on the user's past feedback history. In this way, by referring to the user's past feedback history, the optimal providing method is provided and the learning effect is enhanced.

[0100] The providing unit can customize the content to be provided based on the user's current learning situation when providing feedback. For example, the providing unit customizes the content to be provided based on the user's current learning situation when providing feedback. For example, the providing unit provides optimal feedback taking into account the user's current learning situation. The providing unit can also customize the feedback content according to the user's learning progress. The providing unit can also provide appropriate feedback based on the user's learning situation. For example, the providing unit provides optimal feedback taking into account the user's current learning situation. In this way, more effective feedback can be provided by customizing the content to be provided based on the user's current learning situation.

[0101] The providing unit can improve the providing method by reflecting the user's feedback when providing feedback. For example, the providing unit improves the providing method by reflecting the user's feedback when providing feedback. The providing unit improves the providing method, for example, based on the user's feedback. The providing unit can also suggest an optimal providing method based on the user's feedback. The providing unit can also customize the providing method by reflecting the user's feedback. For example, the providing unit improves the providing method based on the user's feedback. In this way, the providing method is improved by reflecting the user's feedback, and the learning effect is enhanced.

[0102] The providing unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, the providing unit estimates the user's emotions and determines the priority of feedback based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing detailed feedback. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise feedback. Furthermore, if the user is excited, the providing unit can prioritize providing visually stimulating feedback. For example, if the user is relaxed, the providing unit can prioritize providing detailed feedback. In this way, by determining the priority of feedback according to the user's emotions, more effective feedback is provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing feedback. For example, the providing unit selects the optimal providing method by taking into consideration the user's geographical location information when providing feedback. For example, the providing unit provides optimal feedback by taking into consideration the characteristics of the area where the user is currently located. The providing unit can also provide optimal feedback by taking into consideration the characteristics of the area where the user is traveling. The providing unit can also provide optimal feedback based on the user's geographical location information. For example, the providing unit provides optimal feedback by taking into consideration the characteristics of the area where the user is currently located. In this way, the optimal providing method is provided by taking into consideration the user's geographical location information, thereby improving the learning effect.

[0104] The providing unit can analyze the user's social media activity and suggest a means of providing feedback when providing feedback. For example, when providing feedback, the providing unit analyzes the user's social media activity and suggests a means of providing feedback. For example, the providing unit analyzes the user's social media activity and suggests an optimal means of providing feedback. The providing unit can also suggest an appropriate means of providing feedback based on the content of the user's social media posts. The providing unit can also suggest an optimal means of providing feedback by taking into account the activities of the user's friends on social media. For example, the providing unit analyzes the user's social media activity and suggests an optimal means of providing feedback. In this way, by analyzing the user's social media activity, an optimal means of providing feedback is provided, thereby improving learning effectiveness.

[0105] The providing unit can customize the provision method by reflecting the user's past feedback when providing feedback. For example, the providing unit customizes the provision method by reflecting the user's past feedback when providing feedback. For example, the providing unit suggests an optimal provision method based on the user's past feedback. The providing unit can also preferentially suggest a specific provision method based on the user's past feedback. The providing unit can also improve the provision method by reflecting the user's feedback. For example, the providing unit suggests an optimal provision method based on the user's past feedback. In this way, the optimal provision method is provided by reflecting the user's past feedback, thereby improving the learning effect. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information on the user's native language and the language to be learned. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates intermediate pronunciations using a generation AI. The analysis unit is implemented, for example, by the control unit 46A of the smart device 14 and analyzes the user's pronunciation. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives information on the user's native language and the language to be learned. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates intermediate pronunciations using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 and analyzes the user's pronunciation. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives information on the user's native language and the language they wish to learn. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates intermediate pronunciations using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and analyzes the user's pronunciation. The provision unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and provides feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives information on the user's native language and the language they wish to learn. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates intermediate pronunciations using a generation AI. The analysis unit is realized, for example, by the control unit 46A of the robot 414 and analyzes the user's pronunciation. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides feedback.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can propose an optimal study schedule based on the user's study history. For example, the reception unit can analyze the time periods and frequency at which the user studied in the past and propose an optimal study schedule. The reception unit can also dynamically adjust the schedule according to the user's study progress. Furthermore, the reception unit can also propose a study schedule that matches the user's lifestyle. This allows the user to continue studying without strain and effectively correct their pronunciation.

[0108] The generation unit can generate an individually customized pronunciation practice plan based on the user's pronunciation data. For example, the generation unit can identify the user's weak points in pronunciation and generate a practice plan to overcome those weak points. The generation unit can also generate a practice plan that makes use of the user's strong points in pronunciation. Furthermore, the generation unit can generate a practice plan that matches the user's learning style. This allows the user to improve their pronunciation with a practice plan that is optimal for them.

[0109] The analyzer can analyze the user's pronunciation data in real time and provide immediate feedback. For example, the analyzer can analyze the user's pronunciation data immediately after the user pronounces a word and provide immediate feedback. The analyzer can also evaluate the accuracy of the user's pronunciation in real time and point out areas for improvement. Furthermore, the analyzer can track the user's pronunciation progress in real time and provide appropriate feedback. This allows the user to immediately correct their pronunciation and effectively correct their pronunciation.

[0110] The providing unit can introduce gamification elements to increase the user's motivation for pronunciation practice. For example, the providing unit can award points or badges according to the user's progress in pronunciation practice. The providing unit can also provide rewards according to the goals the user achieves. Furthermore, the providing unit can provide a ranking function that allows users to compete with each other. This allows the user to continue pronunciation practice while having fun, and can effectively correct their pronunciation.

[0111] The providing unit can provide a dashboard function to visualize the progress of the user's pronunciation practice. For example, the providing unit can display the progress of the user's pronunciation practice in the form of a graph or chart. The providing unit can also visually display the accuracy of the user's pronunciation and areas for improvement. Furthermore, the providing unit can display the user's pronunciation practice history, allowing the user to review past practice results. This allows the user to understand the progress of their pronunciation practice and effectively correct their pronunciation.

[0112] The reception unit can estimate the user's emotions and adjust the learning content based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide relaxing content. If the user is concentrating, the reception unit can also provide more difficult content. Furthermore, if the user is tired, the reception unit can also provide easier content. In this way, adjusting the learning content according to the user's emotions promotes effective learning.

[0113] The generation unit can estimate the user's emotions and adjust the pace of the pronunciation practice based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can provide pronunciation practice that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also provide effective pronunciation practice in a short amount of time. Furthermore, if the user is excited, the generation unit can also provide pronunciation practice that adds visually stimulating effects. In this way, by adjusting the pace of the pronunciation practice according to the user's emotions, effective pronunciation practice can be provided.

[0114] The analysis unit can estimate the user's emotions and adjust the pronunciation analysis feedback based on the estimated user's emotions. For example, the analysis unit can provide detailed feedback when the user is relaxed. The analysis unit can also provide concise feedback when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating feedback when the user is excited. In this way, by adjusting the feedback according to the user's emotions, effective pronunciation analysis can be provided.

[0115] The providing unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, the providing unit can provide detailed feedback when the user is relaxed. The providing unit can also provide brief feedback when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating feedback when the user is excited. In this way, by adjusting the content of the feedback according to the user's emotions, effective pronunciation practice can be provided.

[0116] The providing unit can estimate the user's emotions and adjust the timing of the feedback based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide immediate feedback. If the user is in a hurry, the providing unit can also provide feedback later. Furthermore, if the user is excited, the providing unit can also provide visually stimulating feedback. In this way, by adjusting the timing of the feedback according to the user's emotions, effective pronunciation practice is provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives information about the user's native language and the language they wish to learn. The user simply selects their native language and the language they wish to learn. Step 2: The generation unit uses a generation AI to generate a pronunciation that is intermediate between the native language and the language to be learned based on the information received by the reception unit. The generation AI generates the intermediate pronunciation using, for example, a text generation AI (e.g., LLM). The generation unit can also generate the intermediate pronunciation based on audio data of the native language and the language to be learned. For example, the generation AI analyzes audio data of Japanese and English and generates an intermediate pronunciation. Step 3: The analyzer analyzes the user's pronunciation based on the intermediate pronunciation generated by the generator. The analyzer analyzes the user's pronunciation and identifies any discrepancies from the correct pronunciation. The analyzer can also analyze the user's pronunciation and evaluate the accuracy of the pronunciation. Step 4: The providing unit provides feedback based on the results of the analysis by the analyzing unit. The providing unit evaluates the accuracy of the user's pronunciation and points out areas for improvement. The providing unit can also provide feedback according to the user's level of proficiency. For example, the providing unit may provide basic pronunciation instruction to beginners, intermediate pronunciation instruction to intermediate learners, and native pronunciation instruction to advanced learners.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[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 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.

[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 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 reception unit that receives information about the user's native language and the language they wish to learn; a generating unit that provides specific explanations of pronunciations intermediate between the native language and the language to be learned based on the information received by the receiving unit; an analysis unit that analyzes the user's pronunciation based on the intermediate pronunciation generated by the generation unit; a providing unit that provides feedback based on the results of the analysis by the analyzing unit. A system characterized by:

2. The generation unit Generate intermediate pronunciations based on speech data from your native language and the language you want to learn 2. The system of claim 1.

3. The analysis unit Analyzes the user's pronunciation and identifies any discrepancies from the correct pronunciation 2. The system of claim 1.

4. The providing unit Providing feedback based on user proficiency 2. The system of claim 1.

5. The generation unit Gradually change the intermediate pronunciation according to the user's proficiency 2. The system of claim 1.

6. The providing unit Tracking the user's pronunciation practice progress and providing appropriate feedback 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of receiving information about the native language and the language the user wants to learn based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past learning history and select the optimal method for receiving language information 2. The system of claim 1.

9. The reception unit When receiving language information, filter it based on the user's current learning status and areas of interest.

2. The system of claim 1.

10. The reception unit When receiving language information, select the most appropriate reception method depending on the user's input method.

2. The system of claim 1.

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