system

Abstract

The system according to the embodiment aims to evaluate pronunciation accuracy in real time and provide appropriate feedback. [Solution] A system according to an embodiment includes a reception unit, a speech recognition unit, an evaluation unit, a feedback unit, and a scenario unit. The reception unit receives pronunciation from a user. The speech recognition unit analyzes the pronunciation received by the reception unit. The evaluation unit evaluates the accuracy of the pronunciation analyzed by the speech recognition unit. The feedback unit feeds back the results of the evaluation by the evaluation unit. The scenario unit provides a conversation based on a business scenario.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to evaluate pronunciation accuracy in real time and provide appropriate feedback.

[0005] The system according to the embodiment aims to evaluate pronunciation accuracy in real time and provide appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a speech recognition unit, an evaluation unit, a feedback unit, and a scenario unit. The reception unit receives pronunciation from a user. The speech recognition unit analyzes the pronunciation received by the reception unit. The evaluation unit evaluates the accuracy of the pronunciation analyzed by the speech recognition unit. The feedback unit feeds back the results of the evaluation by the evaluation unit. The scenario unit provides a conversation based on a business scenario. [Effects of the Invention]

[0007] The system according to the embodiment can assess pronunciation accuracy in real time and provide appropriate feedback. [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 language learning support system according to an embodiment of the present invention analyzes, evaluates, and provides feedback on a user's pronunciation in real time. The system allows a user to initiate a dialogue with an avatar, analyzes the user's pronunciation in real time using speech recognition technology, and evaluates the accuracy at the phoneme level. The evaluation results are provided as visual and audio feedback, allowing the user to correct their pronunciation. The system also includes a business scenario-based conversation function, allowing the user to practice conversations based on actual business situations. For example, conversations based on specific scenarios, such as practicing how to conduct a meeting or give a presentation, are possible. This allows the user to acquire practical language skills. The language learning support system can analyze, evaluate, and provide feedback on a user's pronunciation in real time. For example, the system can instantly analyze the user's pronunciation, evaluate the accuracy, and provide visual and audio feedback, making it easier for the user to correct their pronunciation. Furthermore, by practicing conversations based on business scenarios, the user can acquire practical language skills based on actual business situations.

[0029] A language learning assistance system according to an embodiment includes a reception unit, a speech recognition unit, an evaluation unit, a feedback unit, and a scenario unit. The reception unit receives pronunciation from a user. For example, the reception unit receives words pronounced by the user through a microphone and stores them as speech data. The speech recognition unit analyzes the pronunciation received by the reception unit. For example, the speech recognition unit analyzes the speech data using speech recognition technology and evaluates accuracy at the phoneme level. The evaluation unit evaluates the accuracy of the pronunciation analyzed by the speech recognition unit. For example, the evaluation unit performs evaluation based on the degree of phoneme agreement and the timing of pronunciation. The feedback unit feeds back the results of the evaluation by the evaluation unit. For example, the feedback unit provides the evaluation results visually and audibly to the user. The scenario unit provides conversations based on business scenarios. For example, the scenario unit provides business scenarios such as meeting progress, presentations, and negotiations, allowing the user to practice conversations based on actual business situations. This allows the language learning assistance system according to an embodiment to analyze, evaluate, and provide feedback on the user's pronunciation in real time.

[0030] The speech recognition unit can analyze the user's pronunciation in real time. For example, the speech recognition unit analyzes words pronounced by the user in real time. For example, the speech recognition unit uses speech recognition technology to instantly analyze the user's pronunciation and evaluate accuracy at the phoneme level. The speech recognition unit can also provide instant feedback to the user based on the real-time analysis results, allowing the user to immediately correct their pronunciation. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's pronunciation data into a generation AI and cause the generation AI to perform real-time analysis.

[0031] The evaluation unit can evaluate the pronunciation based on accuracy at the phoneme level. The evaluation unit evaluates the pronunciation based on accuracy at the phoneme level, for example. For example, the evaluation unit performs evaluation based on the degree of phoneme agreement and the timing of pronunciation. The evaluation unit can also perform detailed evaluation based on the type of phoneme and the method of distinguishing phonemes. This allows for a detailed evaluation of the user's pronunciation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input pronunciation data analyzed by the speech recognition unit to the generation AI and cause the generation AI to perform evaluation at the phoneme level.

[0032] The feedback unit can provide visual and audio feedback of the evaluation result. For example, the feedback unit provides visual and audio feedback of the evaluation result. For example, the feedback unit visually provides feedback of the evaluation result using a graph display or a color change. The feedback unit can also provide audio feedback of the evaluation result using a voice message or sound volume. This makes it easier for the user to correct their pronunciation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the results of the evaluation by the evaluation unit to the generation AI and cause the generation AI to provide visual and audio feedback.

[0033] The scenario unit can provide business scenarios such as meeting progress, presentations, and negotiations. The scenario unit can provide, for example, business scenarios such as meeting progress, presentations, and negotiations. For example, the scenario unit can provide the agenda setting necessary for the meeting to proceed and the role of the facilitator. The scenario unit can also provide presentation slide structure and speaking tips. The scenario unit can also provide negotiation strategies and how to proceed with discussions. This allows users to practice conversations based on actual business situations. Some or all of the above-mentioned processing in the scenario unit can be performed using, or without, AI, for example. For example, the scenario unit can input business scenario data into a generation AI and have the generation AI provide the scenario.

[0034] The reception unit can analyze the user's past pronunciation history and select the optimal reception method. The reception unit, for example, analyzes the user's past pronunciation history and selects the optimal reception method. For example, it can prioritize reception of pronunciations that the user has previously struggled with. It can also postpone reception of pronunciations that the user is good at. It can also select the most effective reception method for a specific time period from the user's pronunciation history. In this way, the optimal reception method can be selected by analyzing the user's past pronunciation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's pronunciation history data into a generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can filter the pronunciations based on the user's current learning progress and areas of interest when receiving the pronunciations. For example, the reception unit can prioritize receiving words and phrases that the user is currently learning. It can also prioritize receiving pronunciations related to the user's areas of interest. It can also prioritize receiving pronunciations with higher difficulty levels depending on the user's learning progress. This enables more effective learning by filtering pronunciations based on the user's learning progress and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's learning progress data into a generation AI and have the generation AI perform the filtering.

[0036] The reception unit can select the optimal reception means depending on the user's input method when receiving the pronunciation. For example, the reception unit selects the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving the pronunciation. For example, if the user selects voice input, the pronunciation can be received using voice recognition. Also, if the user selects text input, the pronunciation can be received using text analysis. Also, if the user selects image input, the pronunciation can be received using image analysis. This enables more flexible pronunciation reception by selecting the optimal reception means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and cause the generation AI to select the optimal reception means.

[0037] When receiving a pronunciation, the reception unit can prioritize receiving highly relevant pronunciations by taking into account the user's geographical location information. For example, when receiving a pronunciation, the reception unit prioritizes receiving highly relevant pronunciations by taking into account the user's geographical location information. For example, when the user is in a specific region, the reception unit can prioritize receiving pronunciations related to the language or dialect of that region. Also, when the user is traveling, the reception unit can prioritize receiving pronunciations related to the language of the travel destination. Also, when the user is in a specific country, the reception unit can prioritize receiving pronunciations related to the culture and customs of that country. In this way, by taking the user's geographical location information into account, highly relevant pronunciations can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant pronunciations.

[0038] The reception unit can analyze the user's social media activity and receive related pronunciations when receiving a pronunciation. For example, the reception unit can analyze the user's social media activity and receive related pronunciations when receiving a pronunciation. For example, the reception unit can prioritize receiving words and phrases frequently used by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related pronunciations. The reception unit can also refer to the activity of the user's friends on social media to receive related pronunciations. In this way, by analyzing the user's social media activity, related pronunciations can be prioritized. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related pronunciations.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a pronunciation. The reception unit, for example, customizes the reception method by reflecting the user's past feedback when receiving a pronunciation. For example, the reception unit adjusts the pronunciation reception method based on feedback the user has received in the past. The reception unit can also analyze the content of the user's past feedback and suggest an optimal reception method. The reception unit can also adjust the timing of receiving a pronunciation by referring to the user's past feedback. This enables more effective pronunciation reception by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and cause the generation AI to customize the reception method.

[0040] The speech recognition unit can adjust the level of detail of recognition based on the importance of pronunciation during speech recognition. The speech recognition unit, for example, adjusts the level of detail of recognition based on the importance of pronunciation during speech recognition. For example, for important pronunciations, detailed speech recognition can be performed. Also, for pronunciations with low importance, simplified speech recognition can be performed. The level of detail of speech recognition can also be dynamically adjusted according to the importance of pronunciation. As a result, efficient speech recognition is possible by adjusting the level of detail of recognition based on the importance of pronunciation. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and cause the generation AI to adjust the level of detail of recognition.

[0041] The speech recognition unit can apply different recognition algorithms depending on the pronunciation category during speech recognition. For example, the speech recognition unit applies different recognition algorithms depending on the pronunciation category during speech recognition. For example, for business terms, a specialized speech recognition algorithm is applied. For everyday conversation, a general speech recognition algorithm can also be applied. The optimal speech recognition algorithm can also be selected depending on the pronunciation category. By applying the optimal recognition algorithm depending on the pronunciation category, the accuracy of speech recognition is improved. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and cause the generation AI to apply a recognition algorithm.

[0042] The speech recognition unit can improve the accuracy of speech recognition by referring to the user's past recognition results. For example, the speech recognition unit can improve the accuracy of speech recognition by referring to the user's past recognition results. For example, the speech recognition unit can adjust the accuracy of speech recognition based on the user's past recognition results. The speech recognition unit can also analyze the user's past recognition results and make adjustments to reduce recognition errors. The speech recognition algorithm can also be optimized by referring to the user's past recognition results. In this way, the accuracy of speech recognition is improved by referring to the user's past recognition results. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input past recognition result data into a generation AI and have the generation AI improve the recognition accuracy.

[0043] The speech recognition unit can determine the recognition priority based on the time of submission of the pronunciation during speech recognition. The speech recognition unit, for example, determines the recognition priority based on the time of submission of the pronunciation during speech recognition. For example, the speech recognition unit prioritizes recognition of the most recently submitted pronunciation. It can also prioritize recognition of the pronunciation submitted earlier. It can also dynamically adjust the recognition priority based on the time of submission. This enables efficient speech recognition by determining the recognition priority based on the time of submission of the pronunciation. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and have the generation AI determine the recognition priority.

[0044] The speech recognition unit can adjust the order of recognition based on the relevance of pronunciations during speech recognition. The speech recognition unit, for example, adjusts the order of recognition based on the relevance of pronunciations during speech recognition. For example, highly relevant pronunciations can be recognized preferentially. Also, pronunciations with low relevance can be recognized later. The order of recognition can also be dynamically adjusted based on the relevance of pronunciations. In this way, adjusting the order of recognition based on the relevance of pronunciations enables efficient speech recognition. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and cause the generation AI to adjust the order of recognition.

[0045] The speech recognition unit can adjust the use of technical terms for recognition according to the user's level of expertise during speech recognition. For example, the speech recognition unit can adjust the use of technical terms for recognition according to the user's level of expertise during speech recognition. For example, if the user has technical expertise, the speech recognition unit can use technical terms for recognition. Alternatively, if the user does not have technical expertise, the speech recognition unit can use general terms for recognition. The use of technical terms for recognition can also be dynamically adjusted according to the user's level of expertise. This enables more appropriate speech recognition by adjusting the use of technical terms for recognition according to the user's level of expertise. Some or all of the above-described processing in the speech recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the speech recognition unit can input the user's technical expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0046] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships of pronunciations during evaluation. The evaluation unit, for example, improves the accuracy of the evaluation by taking into account the interrelationships of pronunciations during evaluation. For example, the evaluation is performed by taking into account the context of the pronunciation. The evaluation can also be performed by taking into account the context of the pronunciation. The accuracy of the evaluation can also be improved based on the interrelationships of pronunciation. In this way, the accuracy of the evaluation is improved by taking into account the interrelationships of pronunciation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input pronunciation data to a generation AI and cause the generation AI to evaluate the interrelationships.

[0047] The evaluation unit can perform the evaluation by taking into consideration the attribute information of the person who submitted the pronunciation. For example, the evaluation unit performs the evaluation by taking into consideration the attribute information of the person who submitted the pronunciation. For example, the evaluation is performed by taking into consideration the age of the person who submitted the pronunciation. The evaluation can also be performed by taking into consideration the learning history of the person who submitted the pronunciation. The accuracy of the evaluation can also be improved based on the attribute information of the person who submitted the pronunciation. This makes it possible to perform a more appropriate evaluation by taking into consideration the attribute information of the person who submitted the pronunciation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the attribute data of the person who submitted the pronunciation into the generation AI and cause the generation AI to perform an evaluation based on the attribute information.

[0048] The evaluation unit can weight the evaluation based on the frequency of pronunciation submission during evaluation. The evaluation unit, for example, weights the evaluation based on the frequency of pronunciation submission during evaluation. For example, the evaluation unit may weight pronunciations that are submitted more frequently. The evaluation unit may also weight pronunciations that are submitted less frequently during evaluation. The evaluation weights can also be dynamically adjusted based on the submission frequency. This allows for more appropriate evaluation by weighting the evaluation based on the frequency of pronunciation submission. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input submission frequency data into a generation AI and cause the generation AI to adjust the weights.

[0049] The evaluation unit can perform evaluation taking into account the geographical distribution of pronunciation during evaluation. The evaluation unit, for example, performs evaluation taking into account the geographical distribution of pronunciation during evaluation. For example, the evaluation is performed taking into account regional characteristics of pronunciation. The evaluation criteria for pronunciation can also be adjusted based on the geographical distribution. The evaluation can also be performed taking into account differences in pronunciation between regions. This allows for more appropriate evaluation by taking into account the geographical distribution of pronunciation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input geographical distribution data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0050] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to pronunciation during evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to literature related to pronunciation during evaluation, for example. For example, the evaluation unit can set pronunciation evaluation criteria based on the related literature. The pronunciation can also be evaluated by referring to the related literature. The accuracy of the pronunciation evaluation can also be improved based on the related literature. In this way, the accuracy of the evaluation is improved by referring to literature related to pronunciation. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or can be performed without using AI. For example, the evaluation unit can input related literature data into the generation AI and have the generation AI set the evaluation criteria.

[0051] The evaluation unit can perform the evaluation taking into consideration the market value of the pronunciation when evaluating. For example, the evaluation unit performs the evaluation taking into consideration the market value of the pronunciation when evaluating. For example, the evaluation criteria can be set based on the market value. The pronunciation can also be evaluated based on the market value. The evaluation can also be weighted taking into consideration the market value. This enables a more appropriate evaluation by taking into consideration the market value of the pronunciation. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input market value data into the generation AI and have the generation AI set the evaluation criteria.

[0052] The feedback unit can adjust the level of detail of the feedback based on the importance of the pronunciation when providing feedback. The feedback unit, for example, adjusts the level of detail of the feedback based on the importance of the pronunciation when providing feedback. For example, detailed feedback is provided for important pronunciations. Also, simplified feedback can be provided for pronunciations with low importance. The level of detail of the feedback can also be dynamically adjusted according to the importance of the pronunciation. This enables efficient feedback by adjusting the level of detail of the feedback based on the importance of the pronunciation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input pronunciation data to a generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0053] The feedback unit can apply different feedback algorithms depending on the pronunciation category when providing feedback. For example, the feedback unit applies different feedback algorithms depending on the pronunciation category when providing feedback. For example, in the case of business terms, a specialized feedback algorithm can be applied. In addition, in the case of everyday conversation, a general feedback algorithm can be applied. Furthermore, the optimal feedback algorithm can be selected depending on the pronunciation category. In this way, by applying the optimal feedback algorithm depending on the pronunciation category, the accuracy of feedback is improved. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input pronunciation data to a generation AI and cause the generation AI to apply a feedback algorithm.

[0054] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the feedback unit can adjust the accuracy of the feedback based on the user's past feedback results. The feedback unit can also analyze the user's past feedback results and make adjustments to reduce erroneous feedback. The feedback algorithm can also be optimized by referring to the user's past feedback results. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input past feedback result data into the generation AI and cause the generation AI to improve the feedback accuracy.

[0055] The feedback unit can determine the priority of feedback based on the time of submission of the pronunciation when providing feedback. The feedback unit, for example, determines the priority of feedback based on the time of submission of the pronunciation when providing feedback. For example, the feedback unit prioritizes the most recently submitted pronunciation. Also, the feedback unit can prioritize the most recently submitted pronunciation. Also, the feedback unit can prioritize the most recently submitted pronunciation. Also, the feedback unit can dynamically adjust the priority of feedback based on the time of submission. This enables efficient feedback by determining the priority of feedback based on the time of submission of the pronunciation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the pronunciation data to a generation AI and have the generation AI determine the priority of feedback.

[0056] The feedback unit can adjust the order of feedback based on the relevance of pronunciations when providing feedback. The feedback unit, for example, adjusts the order of feedback based on the relevance of pronunciations when providing feedback. For example, highly relevant pronunciations can be given priority in feedback. Also, less relevant pronunciations can be given later in feedback. The order of feedback can also be dynamically adjusted based on the relevance of pronunciations. This enables efficient feedback by adjusting the order of feedback based on the relevance of pronunciations. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input pronunciation data to a generation AI and cause the generation AI to adjust the order of feedback.

[0057] The feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. For example, the feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. For example, if the user has technical expertise, the feedback can be provided using technical terms. On the other hand, if the user does not have technical expertise, the feedback can be provided using general terms. The use of technical terms in the feedback can also be dynamically adjusted depending on the user's level of expertise. This enables more appropriate feedback by adjusting the use of technical terms in the feedback depending on the user's level of expertise. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's expert knowledge data into a generation AI and cause the generation AI to use technical terms.

[0058] When providing a scenario, the scenario unit can optimize the current scenario by referring to past scenario data. For example, when providing a scenario, the scenario unit optimizes the current scenario by referring to past scenario data. For example, the current scenario is optimized based on past scenario data. The scenario unit can also provide an optimal scenario to the user by referring to past scenario data. It can also analyze past scenario data to improve the accuracy of the current scenario. In this way, the current scenario can be optimized by referring to past scenario data. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input past scenario data to a generation AI and cause the generation AI to optimize the current scenario.

[0059] The scenario unit can apply different scenario providing methods to each pronunciation category when providing a scenario. For example, the scenario unit applies different scenario providing methods to each pronunciation category when providing a scenario. For example, in the case of business terms, a specialized scenario providing method is applied. Also, in the case of everyday conversation, a general scenario providing method can be applied. Furthermore, the optimal scenario providing method can be selected depending on the pronunciation category. In this way, by applying different scenario providing methods to each pronunciation category, more appropriate scenarios can be provided. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input pronunciation data to a generation AI and cause the generation AI to apply a scenario providing method.

[0060] When providing a scenario, the scenario unit can provide the scenario taking into consideration the attribute information of the person who submitted the pronunciation. For example, when providing a scenario, the scenario unit provides the scenario taking into consideration the attribute information of the person who submitted the pronunciation. For example, the scenario unit provides the scenario taking into consideration the age of the person who submitted the scenario. It can also provide a scenario taking into consideration the learning history of the person who submitted the scenario. It can also provide an optimal scenario based on the attribute information of the person who submitted the scenario. This makes it possible to provide a more appropriate scenario by taking into consideration the attribute information of the person who submitted the pronunciation. Some or all of the above-mentioned processing in the scenario unit may be performed using AI, for example, or may be performed without using AI. For example, the scenario unit can input the attribute data of the person who submitted the scenario to the generation AI and cause the generation AI to provide a scenario based on the attribute information.

[0061] The scenario unit can analyze changes in the scenario based on the time of submission of the pronunciation when providing the scenario. For example, the scenario unit can analyze changes in the scenario based on the time of submission of the pronunciation when providing the scenario. For example, the scenario unit can analyze changes in the scenario based on pronunciations that were submitted earlier. It can also analyze changes in the scenario based on pronunciations that were submitted more recently. It can also dynamically analyze changes in the scenario based on the time of submission. This makes it possible to provide a more appropriate scenario by analyzing changes in the scenario based on the time of submission of the pronunciation. Some or all of the above-mentioned processing in the scenario unit can be performed using, for example, AI, or can be performed without using AI. For example, the scenario unit can input pronunciation data to a generation AI and cause the generation AI to analyze changes in the scenario.

[0062] The scenario unit can analyze the scenario by referring to market data related to pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario by referring to market data related to pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario based on the relevant market data. The accuracy of the scenario can also be improved by referring to the relevant market data. Changes in the scenario can also be analyzed based on the relevant market data. In this way, the accuracy of the scenario is improved by referring to market data related to pronunciation. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input the relevant market data into the generation AI and have the generation AI perform scenario analysis.

[0063] The scenario unit can analyze the scenario taking into account the technical maturity of pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario taking into account the technical maturity of pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario based on the technical maturity of pronunciation. The accuracy of the scenario can also be improved based on the technical maturity. The scenario can also be analyzed by taking into account the technical maturity of pronunciation. In this way, by taking the technical maturity of pronunciation into account, it becomes possible to provide a more appropriate scenario. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input technical maturity data into a generation AI and cause the generation AI to analyze the scenario.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] When accepting the user's pronunciation, the accepting unit can adjust the difficulty of the pronunciation based on the user's past pronunciation data. For example, by prioritizing the acceptance of pronunciations that the user has had difficulty with in the past, the user's pronunciation skills can be improved. Also, by accepting pronunciations that the user is good at later, the learning can be balanced. Furthermore, the accepting unit can analyze the user's pronunciation data and suggest the most effective pronunciation practice for a specific time period. This makes it possible to utilize the user's past pronunciation data to practice pronunciation more effectively.

[0066] When analyzing a user's pronunciation in real time, the speech recognition unit can adjust the accuracy of the analysis according to the user's pronunciation speed. For example, if the user speaks quickly, the analysis accuracy can be increased to reduce misrecognition. On the other hand, if the user speaks slowly, analysis can be performed with normal accuracy. Furthermore, by providing immediate feedback on the analysis results according to the user's pronunciation speed, it is possible to make it easier for the user to correct their pronunciation. This enables flexible analysis according to the user's pronunciation speed.

[0067] When evaluating pronunciation based on accuracy at the phoneme level, the evaluation unit can take into account the consistency of the user's pronunciation. For example, if a user repeatedly pronounces the same phoneme, the evaluation can reflect that consistency. Also, if a user consecutively pronounces different phonemes, the evaluation can take that continuity into account. Furthermore, detailed feedback can be provided based on the consistency of the user's pronunciation, making it easier for the user to improve their pronunciation. This makes it possible to perform evaluation that takes into account the consistency of the user's pronunciation.

[0068] When providing visual and audio feedback of the evaluation results, the feedback unit can adjust the form of feedback according to the user's learning style. For example, visual learners can receive feedback using graphs or color changes, while auditory learners can receive feedback using audio messages or sound intensity. The level of detail of the feedback can also be adjusted according to the user's learning style. This enables effective feedback according to the user's learning style.

[0069] When providing conversations based on business scenarios, the scenario module can customize the scenarios according to the user's occupation and industry. For example, if the user works in sales, it can provide scenarios for sales talks and presentations. If the user works in engineering, it can also provide scenarios for technical explanations and problem solving. Furthermore, by providing scenarios specialized for the user's industry, more practical conversation practice becomes possible. This makes it possible to provide scenarios customized according to the user's occupation and industry.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives pronunciation from the user. For example, the words pronounced by the user are received by a microphone and stored as voice data. Step 2: The speech recognition unit analyzes the pronunciation received by the reception unit. For example, the speech recognition unit analyzes the speech data using speech recognition technology and evaluates accuracy at the phoneme level. Step 3: The evaluation unit evaluates the accuracy of the pronunciation analyzed by the speech recognition unit. For example, the evaluation unit may evaluate based on the degree of phoneme agreement and the timing of pronunciation. Step 4: The feedback unit feeds back the results of the evaluation by the evaluation unit. For example, the feedback unit provides the evaluation results to the user visually and audibly. Step 5: The scenario module provides conversations based on business scenarios. For example, the scenario module provides business scenarios such as meeting progress, presentations, and negotiations, allowing users to practice conversations based on actual business situations.

[0072] (Example 2) A language learning support system according to an embodiment of the present invention analyzes, evaluates, and provides feedback on a user's pronunciation in real time. The system allows a user to initiate a dialogue with an avatar, analyzes the user's pronunciation in real time using speech recognition technology, and evaluates the accuracy at the phoneme level. The evaluation results are provided as visual and audio feedback, allowing the user to correct their pronunciation. The system also includes a business scenario-based conversation function, allowing the user to practice conversations based on actual business situations. For example, conversations based on specific scenarios, such as practicing how to conduct a meeting or give a presentation, are possible. This allows the user to acquire practical language skills. The language learning support system can analyze, evaluate, and provide feedback on a user's pronunciation in real time. For example, the system can instantly analyze the user's pronunciation, evaluate the accuracy, and provide visual and audio feedback, making it easier for the user to correct their pronunciation. Furthermore, by practicing conversations based on business scenarios, the user can acquire practical language skills based on actual business situations.

[0073] A language learning assistance system according to an embodiment includes a reception unit, a speech recognition unit, an evaluation unit, a feedback unit, and a scenario unit. The reception unit receives pronunciation from a user. For example, the reception unit receives words pronounced by the user through a microphone and stores them as speech data. The speech recognition unit analyzes the pronunciation received by the reception unit. For example, the speech recognition unit analyzes the speech data using speech recognition technology and evaluates accuracy at the phoneme level. The evaluation unit evaluates the accuracy of the pronunciation analyzed by the speech recognition unit. For example, the evaluation unit performs evaluation based on the degree of phoneme agreement and the timing of pronunciation. The feedback unit feeds back the results of the evaluation by the evaluation unit. For example, the feedback unit provides the evaluation results visually and audibly to the user. The scenario unit provides conversations based on business scenarios. For example, the scenario unit provides business scenarios such as meeting progress, presentations, and negotiations, allowing the user to practice conversations based on actual business situations. This allows the language learning assistance system according to an embodiment to analyze, evaluate, and provide feedback on the user's pronunciation in real time.

[0074] The speech recognition unit can analyze the user's pronunciation in real time. For example, the speech recognition unit analyzes words pronounced by the user in real time. For example, the speech recognition unit uses speech recognition technology to instantly analyze the user's pronunciation and evaluate accuracy at the phoneme level. The speech recognition unit can also provide instant feedback to the user based on the real-time analysis results, allowing the user to immediately correct their pronunciation. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's pronunciation data into a generation AI and cause the generation AI to perform real-time analysis.

[0075] The evaluation unit can evaluate the pronunciation based on accuracy at the phoneme level. The evaluation unit evaluates the pronunciation based on accuracy at the phoneme level, for example. For example, the evaluation unit performs evaluation based on the degree of phoneme agreement and the timing of pronunciation. The evaluation unit can also perform detailed evaluation based on the type of phoneme and the method of distinguishing phonemes. This allows for a detailed evaluation of the user's pronunciation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input pronunciation data analyzed by the speech recognition unit to the generation AI and cause the generation AI to perform evaluation at the phoneme level.

[0076] The feedback unit can provide visual and audio feedback of the evaluation result. For example, the feedback unit provides visual and audio feedback of the evaluation result. For example, the feedback unit visually provides feedback of the evaluation result using a graph display or a color change. The feedback unit can also provide audio feedback of the evaluation result using a voice message or sound volume. This makes it easier for the user to correct their pronunciation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the results of the evaluation by the evaluation unit to the generation AI and cause the generation AI to provide visual and audio feedback.

[0077] The scenario unit can provide business scenarios such as meeting progress, presentations, and negotiations. The scenario unit can provide, for example, business scenarios such as meeting progress, presentations, and negotiations. For example, the scenario unit can provide the agenda setting necessary for the meeting to proceed and the role of the facilitator. The scenario unit can also provide presentation slide structure and speaking tips. The scenario unit can also provide negotiation strategies and how to proceed with discussions. This allows users to practice conversations based on actual business situations. Some or all of the above-mentioned processing in the scenario unit can be performed using, or without, AI, for example. For example, the scenario unit can input business scenario data into a generation AI and have the generation AI provide the scenario.

[0078] The language learning assistance system further includes a reception unit that estimates the user's emotion and adjusts the timing of receiving the pronunciation based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of receiving the pronunciation based on the estimated user's emotion. For example, if the user is nervous, the reception unit may wait a short time to allow the user to relax before receiving the pronunciation. Alternatively, if the user is relaxed, the reception unit may immediately receive the pronunciation. Alternatively, if the user is impatient, the reception unit may quickly receive the pronunciation. This allows the timing of receiving the pronunciation to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, or may be performed without an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The reception unit can analyze the user's past pronunciation history and select the optimal reception method. The reception unit, for example, analyzes the user's past pronunciation history and selects the optimal reception method. For example, it can prioritize reception of pronunciations that the user has previously struggled with. It can also postpone reception of pronunciations that the user is good at. It can also select the most effective reception method for a specific time period from the user's pronunciation history. In this way, the optimal reception method can be selected by analyzing the user's past pronunciation history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's pronunciation history data into a generation AI and have the generation AI select the optimal reception method.

[0080] The reception unit can filter the pronunciations based on the user's current learning progress and areas of interest when receiving the pronunciations. For example, the reception unit can prioritize receiving words and phrases that the user is currently learning. It can also prioritize receiving pronunciations related to the user's areas of interest. It can also prioritize receiving pronunciations with higher difficulty levels depending on the user's learning progress. This enables more effective learning by filtering pronunciations based on the user's learning progress and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's learning progress data into a generation AI and have the generation AI perform the filtering.

[0081] The reception unit can select the optimal reception means depending on the user's input method when receiving the pronunciation. For example, the reception unit selects the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving the pronunciation. For example, if the user selects voice input, the pronunciation can be received using voice recognition. Also, if the user selects text input, the pronunciation can be received using text analysis. Also, if the user selects image input, the pronunciation can be received using image analysis. This enables more flexible pronunciation reception by selecting the optimal reception means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and cause the generation AI to select the optimal reception means.

[0082] The reception unit can estimate the user's emotion and determine the priority of pronunciations to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of pronunciations to be received based on the estimated user's emotion. For example, if the user is nervous, easy pronunciations can be received preferentially. Also, if the user is relaxed, difficult pronunciations can be received preferentially. Also, if the user is impatient, short pronunciations can be received preferentially. This makes it possible to determine the priority of pronunciations according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] When receiving a pronunciation, the reception unit can prioritize receiving highly relevant pronunciations by taking into account the user's geographical location information. For example, when receiving a pronunciation, the reception unit prioritizes receiving highly relevant pronunciations by taking into account the user's geographical location information. For example, when the user is in a specific region, the reception unit can prioritize receiving pronunciations related to the language or dialect of that region. Also, when the user is traveling, the reception unit can prioritize receiving pronunciations related to the language of the travel destination. Also, when the user is in a specific country, the reception unit can prioritize receiving pronunciations related to the culture and customs of that country. In this way, by taking the user's geographical location information into account, highly relevant pronunciations can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to select highly relevant pronunciations.

[0084] The reception unit can analyze the user's social media activity and receive related pronunciations when receiving a pronunciation. For example, the reception unit can analyze the user's social media activity and receive related pronunciations when receiving a pronunciation. For example, the reception unit can prioritize receiving words and phrases frequently used by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related pronunciations. The reception unit can also refer to the activity of the user's friends on social media to receive related pronunciations. In this way, by analyzing the user's social media activity, related pronunciations can be prioritized. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to select related pronunciations.

[0085] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a pronunciation. The reception unit, for example, customizes the reception method by reflecting the user's past feedback when receiving a pronunciation. For example, the reception unit adjusts the pronunciation reception method based on feedback the user has received in the past. The reception unit can also analyze the content of the user's past feedback and suggest an optimal reception method. The reception unit can also adjust the timing of receiving a pronunciation by referring to the user's past feedback. This enables more effective pronunciation reception by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and cause the generation AI to customize the reception method.

[0086] The speech recognition unit can estimate the user's emotion and adjust the accuracy of speech recognition based on the estimated user's emotion. For example, the speech recognition unit can estimate the user's emotion and adjust the accuracy of speech recognition based on the estimated user's emotion. For example, if the user is nervous, the speech recognition accuracy can be increased to reduce misrecognition. Also, if the user is relaxed, the speech recognition accuracy can be returned to normal. Also, if the user is impatient, the speech recognition accuracy can be increased to quickly analyze. This allows for adjusting the speech recognition accuracy according to the user's emotion to reduce misrecognition. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit can be performed using, for example, AI, or without AI. For example, the speech recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] The speech recognition unit can adjust the level of detail of recognition based on the importance of pronunciation during speech recognition. The speech recognition unit, for example, adjusts the level of detail of recognition based on the importance of pronunciation during speech recognition. For example, for important pronunciations, detailed speech recognition can be performed. Also, for pronunciations with low importance, simplified speech recognition can be performed. The level of detail of speech recognition can also be dynamically adjusted according to the importance of pronunciation. As a result, efficient speech recognition is possible by adjusting the level of detail of recognition based on the importance of pronunciation. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and cause the generation AI to adjust the level of detail of recognition.

[0088] The speech recognition unit can apply different recognition algorithms depending on the pronunciation category during speech recognition. For example, the speech recognition unit applies different recognition algorithms depending on the pronunciation category during speech recognition. For example, for business terms, a specialized speech recognition algorithm is applied. For everyday conversation, a general speech recognition algorithm can also be applied. The optimal speech recognition algorithm can also be selected depending on the pronunciation category. By applying the optimal recognition algorithm depending on the pronunciation category, the accuracy of speech recognition is improved. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and cause the generation AI to apply a recognition algorithm.

[0089] The speech recognition unit can improve the accuracy of speech recognition by referring to the user's past recognition results. For example, the speech recognition unit can improve the accuracy of speech recognition by referring to the user's past recognition results. For example, the speech recognition unit can adjust the accuracy of speech recognition based on the user's past recognition results. The speech recognition unit can also analyze the user's past recognition results and make adjustments to reduce recognition errors. The speech recognition algorithm can also be optimized by referring to the user's past recognition results. In this way, the accuracy of speech recognition is improved by referring to the user's past recognition results. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input past recognition result data into a generation AI and have the generation AI improve the recognition accuracy.

[0090] The speech recognition unit can estimate the user's emotion and adjust the length of the speech recognition based on the estimated user's emotion. For example, the speech recognition unit estimates the user's emotion and adjusts the length of the speech recognition based on the estimated user's emotion. For example, if the user is nervous, the speech recognition unit performs short speech recognition. Also, if the user is relaxed, the speech recognition unit can perform normal-length speech recognition. Also, if the user is impatient, the speech recognition unit can perform short speech recognition. This enables more appropriate speech recognition by adjusting the length of the speech recognition according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit may be performed using AI, or may be performed without AI. For example, the speech recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0091] The speech recognition unit can determine the recognition priority based on the time of submission of the pronunciation during speech recognition. The speech recognition unit, for example, determines the recognition priority based on the time of submission of the pronunciation during speech recognition. For example, the speech recognition unit prioritizes recognition of the most recently submitted pronunciation. It can also prioritize recognition of the pronunciation submitted earlier. It can also dynamically adjust the recognition priority based on the time of submission. This enables efficient speech recognition by determining the recognition priority based on the time of submission of the pronunciation. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and have the generation AI determine the recognition priority.

[0092] The speech recognition unit can adjust the order of recognition based on the relevance of pronunciations during speech recognition. The speech recognition unit, for example, adjusts the order of recognition based on the relevance of pronunciations during speech recognition. For example, highly relevant pronunciations can be recognized preferentially. Also, pronunciations with low relevance can be recognized later. The order of recognition can also be dynamically adjusted based on the relevance of pronunciations. In this way, adjusting the order of recognition based on the relevance of pronunciations enables efficient speech recognition. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input pronunciation data to a generation AI and cause the generation AI to adjust the order of recognition.

[0093] The speech recognition unit can adjust the use of technical terms for recognition according to the user's level of expertise during speech recognition. For example, the speech recognition unit can adjust the use of technical terms for recognition according to the user's level of expertise during speech recognition. For example, if the user has technical expertise, the speech recognition unit can use technical terms for recognition. Alternatively, if the user does not have technical expertise, the speech recognition unit can use general terms for recognition. The use of technical terms for recognition can also be dynamically adjusted according to the user's level of expertise. This enables more appropriate speech recognition by adjusting the use of technical terms for recognition according to the user's level of expertise. Some or all of the above-described processing in the speech recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the speech recognition unit can input the user's technical expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0094] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is nervous, the evaluation criteria can be relaxed. Alternatively, if the user is relaxed, normal evaluation criteria can be applied. Alternatively, if the user is impatient, the evaluation criteria can be relaxed. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using an AI, for example, or without an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships of pronunciations during evaluation. The evaluation unit, for example, improves the accuracy of the evaluation by taking into account the interrelationships of pronunciations during evaluation. For example, the evaluation is performed by taking into account the context of the pronunciation. The evaluation can also be performed by taking into account the context of the pronunciation. The accuracy of the evaluation can also be improved based on the interrelationships of pronunciation. In this way, the accuracy of the evaluation is improved by taking into account the interrelationships of pronunciation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input pronunciation data to a generation AI and cause the generation AI to evaluate the interrelationships.

[0096] The evaluation unit can perform the evaluation by taking into consideration the attribute information of the person who submitted the pronunciation. For example, the evaluation unit performs the evaluation by taking into consideration the attribute information of the person who submitted the pronunciation. For example, the evaluation is performed by taking into consideration the age of the person who submitted the pronunciation. The evaluation can also be performed by taking into consideration the learning history of the person who submitted the pronunciation. The accuracy of the evaluation can also be improved based on the attribute information of the person who submitted the pronunciation. This makes it possible to perform a more appropriate evaluation by taking into consideration the attribute information of the person who submitted the pronunciation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the attribute data of the person who submitted the pronunciation into the generation AI and cause the generation AI to perform an evaluation based on the attribute information.

[0097] The evaluation unit can weight the evaluation based on the frequency of pronunciation submission during evaluation. The evaluation unit, for example, weights the evaluation based on the frequency of pronunciation submission during evaluation. For example, the evaluation unit may weight pronunciations that are submitted more frequently. The evaluation unit may also weight pronunciations that are submitted less frequently during evaluation. The evaluation weights can also be dynamically adjusted based on the submission frequency. This allows for more appropriate evaluation by weighting the evaluation based on the frequency of pronunciation submission. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input submission frequency data into a generation AI and cause the generation AI to adjust the weights.

[0098] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. For example, the evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. For example, if the user is nervous, simple evaluation results can be displayed first. Also, if the user is relaxed, detailed evaluation results can be displayed first. Also, if the user is impatient, important evaluation results can be displayed first. This allows for more appropriate feedback by adjusting the order in which the evaluation results are displayed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or without an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The evaluation unit can perform evaluation taking into account the geographical distribution of pronunciation during evaluation. The evaluation unit, for example, performs evaluation taking into account the geographical distribution of pronunciation during evaluation. For example, the evaluation is performed taking into account regional characteristics of pronunciation. The evaluation criteria for pronunciation can also be adjusted based on the geographical distribution. The evaluation can also be performed taking into account differences in pronunciation between regions. This allows for more appropriate evaluation by taking into account the geographical distribution of pronunciation. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input geographical distribution data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0100] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to pronunciation during evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to literature related to pronunciation during evaluation, for example. For example, the evaluation unit can set pronunciation evaluation criteria based on the related literature. The pronunciation can also be evaluated by referring to the related literature. The accuracy of the pronunciation evaluation can also be improved based on the related literature. In this way, the accuracy of the evaluation is improved by referring to literature related to pronunciation. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or can be performed without using AI. For example, the evaluation unit can input related literature data into the generation AI and have the generation AI set the evaluation criteria.

[0101] The evaluation unit can perform the evaluation taking into consideration the market value of the pronunciation when evaluating. For example, the evaluation unit performs the evaluation taking into consideration the market value of the pronunciation when evaluating. For example, the evaluation criteria can be set based on the market value. The pronunciation can also be evaluated based on the market value. The evaluation can also be weighted taking into consideration the market value. This enables a more appropriate evaluation by taking into consideration the market value of the pronunciation. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input market value data into the generation AI and have the generation AI set the evaluation criteria.

[0102] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is impatient, the feedback unit can provide concise feedback. This allows for more appropriate feedback by adjusting the feedback expression method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0103] The feedback unit can adjust the level of detail of the feedback based on the importance of the pronunciation when providing feedback. The feedback unit, for example, adjusts the level of detail of the feedback based on the importance of the pronunciation when providing feedback. For example, detailed feedback is provided for important pronunciations. Also, simplified feedback can be provided for pronunciations with low importance. The level of detail of the feedback can also be dynamically adjusted according to the importance of the pronunciation. This enables efficient feedback by adjusting the level of detail of the feedback based on the importance of the pronunciation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input pronunciation data to a generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0104] The feedback unit can apply different feedback algorithms depending on the pronunciation category when providing feedback. For example, the feedback unit applies different feedback algorithms depending on the pronunciation category when providing feedback. For example, in the case of business terms, a specialized feedback algorithm can be applied. In addition, in the case of everyday conversation, a general feedback algorithm can be applied. Furthermore, the optimal feedback algorithm can be selected depending on the pronunciation category. In this way, by applying the optimal feedback algorithm depending on the pronunciation category, the accuracy of feedback is improved. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input pronunciation data to a generation AI and cause the generation AI to apply a feedback algorithm.

[0105] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the feedback unit can adjust the accuracy of the feedback based on the user's past feedback results. The feedback unit can also analyze the user's past feedback results and make adjustments to reduce erroneous feedback. The feedback algorithm can also be optimized by referring to the user's past feedback results. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input past feedback result data into the generation AI and cause the generation AI to improve the feedback accuracy.

[0106] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the feedback unit estimates the user's emotion and adjusts the length of the feedback based on the estimated user's emotion. For example, if the user is nervous, short feedback can be provided. Also, if the user is relaxed, normal length feedback can be provided. Also, if the user is impatient, short feedback can be provided. This allows for more appropriate feedback by adjusting the length of the feedback 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0107] The feedback unit can determine the priority of feedback based on the time of submission of the pronunciation when providing feedback. The feedback unit, for example, determines the priority of feedback based on the time of submission of the pronunciation when providing feedback. For example, the feedback unit prioritizes the most recently submitted pronunciation. Also, the feedback unit can prioritize the most recently submitted pronunciation. Also, the feedback unit can prioritize the most recently submitted pronunciation. Also, the feedback unit can dynamically adjust the priority of feedback based on the time of submission. This enables efficient feedback by determining the priority of feedback based on the time of submission of the pronunciation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the pronunciation data to a generation AI and have the generation AI determine the priority of feedback.

[0108] The feedback unit can adjust the order of feedback based on the relevance of pronunciations when providing feedback. The feedback unit, for example, adjusts the order of feedback based on the relevance of pronunciations when providing feedback. For example, highly relevant pronunciations can be given priority in feedback. Also, less relevant pronunciations can be given later in feedback. The order of feedback can also be dynamically adjusted based on the relevance of pronunciations. This enables efficient feedback by adjusting the order of feedback based on the relevance of pronunciations. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input pronunciation data to a generation AI and cause the generation AI to adjust the order of feedback.

[0109] The feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. For example, the feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise when providing feedback. For example, if the user has technical expertise, the feedback can be provided using technical terms. On the other hand, if the user does not have technical expertise, the feedback can be provided using general terms. The use of technical terms in the feedback can also be dynamically adjusted depending on the user's level of expertise. This enables more appropriate feedback by adjusting the use of technical terms in the feedback depending on the user's level of expertise. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's expert knowledge data into a generation AI and cause the generation AI to use technical terms.

[0110] The scenario unit can estimate the user's emotions and adjust the display method of the scenario based on the estimated user emotions. For example, the scenario unit can estimate the user's emotions and adjust the display method of the scenario based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate scenario display by adjusting the display method of the scenario according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the scenario unit can be performed using AI, for example, or without AI. For example, the scenario unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0111] When providing a scenario, the scenario unit can optimize the current scenario by referring to past scenario data. For example, when providing a scenario, the scenario unit optimizes the current scenario by referring to past scenario data. For example, the current scenario is optimized based on past scenario data. The scenario unit can also provide an optimal scenario to the user by referring to past scenario data. It can also analyze past scenario data to improve the accuracy of the current scenario. In this way, the current scenario can be optimized by referring to past scenario data. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input past scenario data to a generation AI and cause the generation AI to optimize the current scenario.

[0112] The scenario unit can apply different scenario providing methods to each pronunciation category when providing a scenario. For example, the scenario unit applies different scenario providing methods to each pronunciation category when providing a scenario. For example, in the case of business terms, a specialized scenario providing method is applied. Also, in the case of everyday conversation, a general scenario providing method can be applied. Furthermore, the optimal scenario providing method can be selected depending on the pronunciation category. In this way, by applying different scenario providing methods to each pronunciation category, more appropriate scenarios can be provided. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input pronunciation data to a generation AI and cause the generation AI to apply a scenario providing method.

[0113] When providing a scenario, the scenario unit can provide the scenario taking into consideration the attribute information of the person who submitted the pronunciation. For example, when providing a scenario, the scenario unit provides the scenario taking into consideration the attribute information of the person who submitted the pronunciation. For example, the scenario unit provides the scenario taking into consideration the age of the person who submitted the scenario. It can also provide a scenario taking into consideration the learning history of the person who submitted the scenario. It can also provide an optimal scenario based on the attribute information of the person who submitted the scenario. This makes it possible to provide a more appropriate scenario by taking into consideration the attribute information of the person who submitted the pronunciation. Some or all of the above-mentioned processing in the scenario unit may be performed using AI, for example, or may be performed without using AI. For example, the scenario unit can input the attribute data of the person who submitted the scenario to the generation AI and cause the generation AI to provide a scenario based on the attribute information.

[0114] The scenario unit can estimate the user's emotions and adjust the importance of a scenario based on the estimated user's emotions. For example, the scenario unit can estimate the user's emotions and adjust the importance of a scenario based on the estimated user's emotions. For example, if the user is nervous, the scenario unit can provide a scenario with a low importance. Also, if the user is relaxed, the scenario unit can provide a scenario with a high importance. Also, if the user is impatient, the scenario unit can provide a scenario with a low importance. This allows for adjusting the importance of a scenario according to the user's emotions, thereby providing a more appropriate scenario. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the scenario unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the scenario unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0115] The scenario unit can analyze changes in the scenario based on the time of submission of the pronunciation when providing the scenario. For example, the scenario unit can analyze changes in the scenario based on the time of submission of the pronunciation when providing the scenario. For example, the scenario unit can analyze changes in the scenario based on pronunciations that were submitted earlier. It can also analyze changes in the scenario based on pronunciations that were submitted more recently. It can also dynamically analyze changes in the scenario based on the time of submission. This makes it possible to provide a more appropriate scenario by analyzing changes in the scenario based on the time of submission of the pronunciation. Some or all of the above-mentioned processing in the scenario unit can be performed using, for example, AI, or can be performed without using AI. For example, the scenario unit can input pronunciation data to a generation AI and cause the generation AI to analyze changes in the scenario.

[0116] The scenario unit can analyze the scenario by referring to market data related to pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario by referring to market data related to pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario based on the relevant market data. The accuracy of the scenario can also be improved by referring to the relevant market data. Changes in the scenario can also be analyzed based on the relevant market data. In this way, the accuracy of the scenario is improved by referring to market data related to pronunciation. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input the relevant market data into the generation AI and have the generation AI perform scenario analysis.

[0117] The scenario unit can analyze the scenario taking into account the technical maturity of pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario taking into account the technical maturity of pronunciation when providing the scenario. For example, the scenario unit analyzes the scenario based on the technical maturity of pronunciation. The accuracy of the scenario can also be improved based on the technical maturity. The scenario can also be analyzed by taking into account the technical maturity of pronunciation. In this way, by taking the technical maturity of pronunciation into account, it becomes possible to provide a more appropriate scenario. Some or all of the above-mentioned processing in the scenario unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario unit can input technical maturity data into a generation AI and cause the generation AI to analyze the scenario. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, speech recognition unit, evaluation unit, feedback unit, and scenario 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 receives a user's pronunciation using the microphone 38B of the smart device 14 and stores it as speech data. The speech recognition unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the speech data to evaluate accuracy at the phoneme level. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the degree of phoneme agreement and pronunciation timing. The feedback unit provides the evaluation results to the user visually and audibly using the display 40A and speaker 40B of the smart device 14. The scenario unit is implemented by the specific processing unit 290 of the data processing device 12 and provides conversations based on business scenarios. Furthermore, the reception unit has the function of estimating the user's emotions and adjusting the timing of receiving pronunciation based on the estimated emotions, for example, by acquiring the user's facial expression data using the camera 42 of the smart device 14 and estimating the emotions using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, speech recognition unit, evaluation unit, feedback unit, and scenario unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's pronunciation using the microphone 238 of the smart glasses 214 and stores it as speech data. The speech recognition unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the speech data to evaluate accuracy at the phoneme level. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the degree of phoneme agreement and pronunciation timing. The feedback unit provides the evaluation result to the user audibly using the speaker 240 of the smart glasses 214. The scenario unit is realized by the specific processing unit 290 of the data processing device 12 and provides conversation based on a business scenario. Furthermore, the reception unit has the function of estimating the user's emotions and adjusting the timing of receiving the pronunciation based on the estimated emotions, and for example, acquires the user's facial expression data using the camera 42 of the smart glasses 214 and estimates the emotions using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, speech recognition unit, evaluation unit, feedback unit, and scenario 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 receives a user's pronunciation using the microphone 238 of the headset-type terminal 314 and saves it as voice data. The voice recognition unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice data to evaluate accuracy at the phoneme level. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and performs evaluation based on the degree of phoneme agreement and the timing of pronunciation. The feedback unit provides the evaluation result to the user audibly using the speaker 240 of the headset-type terminal 314. The scenario unit is realized by the specific processing unit 290 of the data processing device 12 and provides conversation based on a business scenario. Furthermore, the reception unit has the function of estimating the user's emotions and adjusting the timing of receiving pronunciation based on the estimated emotions, and for example, acquires the user's facial expression data using the camera 42 of the headset-type terminal 314 and estimates the emotions using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, speech recognition unit, evaluation unit, feedback unit, and scenario unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's pronunciation using the microphone 238 of the robot 414 and saves it as speech data. The speech recognition unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the speech data and evaluates accuracy at the phoneme level. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, performs evaluation based on the degree of phoneme agreement and pronunciation timing. The feedback unit provides the evaluation result to the user audibly using the speaker 240 of the robot 414. The scenario unit, realized by the specific processing unit 290 of the data processing device 12, provides conversation based on a business scenario. Furthermore, the reception unit has a function of estimating the user's emotions and adjusting the timing of pronunciation acceptance based on the estimated emotions. For example, the reception unit acquires the user's facial expression data using the camera 42 of the robot 414 and estimates the user's emotions using the specific processing unit 290 of the data processing device 12.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] When accepting the user's pronunciation, the accepting unit can adjust the difficulty of the pronunciation based on the user's past pronunciation data. For example, by prioritizing the acceptance of pronunciations that the user has had difficulty with in the past, the user's pronunciation skills can be improved. Also, by accepting pronunciations that the user is good at later, the learning can be balanced. Furthermore, the accepting unit can analyze the user's pronunciation data and suggest the most effective pronunciation practice for a specific time period. This makes it possible to utilize the user's past pronunciation data to practice pronunciation more effectively.

[0120] When analyzing a user's pronunciation in real time, the speech recognition unit can adjust the accuracy of the analysis according to the user's pronunciation speed. For example, if the user speaks quickly, the analysis accuracy can be increased to reduce misrecognition. On the other hand, if the user speaks slowly, analysis can be performed with normal accuracy. Furthermore, by providing immediate feedback on the analysis results according to the user's pronunciation speed, it is possible to make it easier for the user to correct their pronunciation. This enables flexible analysis according to the user's pronunciation speed.

[0121] When evaluating pronunciation based on accuracy at the phoneme level, the evaluation unit can take into account the consistency of the user's pronunciation. For example, if a user repeatedly pronounces the same phoneme, the evaluation can reflect that consistency. Also, if a user consecutively pronounces different phonemes, the evaluation can take that continuity into account. Furthermore, detailed feedback can be provided based on the consistency of the user's pronunciation, making it easier for the user to improve their pronunciation. This makes it possible to perform evaluation that takes into account the consistency of the user's pronunciation.

[0122] When providing visual and audio feedback of the evaluation results, the feedback unit can adjust the form of feedback according to the user's learning style. For example, visual learners can receive feedback using graphs or color changes, while auditory learners can receive feedback using audio messages or sound intensity. The level of detail of the feedback can also be adjusted according to the user's learning style. This enables effective feedback according to the user's learning style.

[0123] When providing conversations based on business scenarios, the scenario module can customize the scenarios according to the user's occupation and industry. For example, if the user works in sales, it can provide scenarios for sales talks and presentations. If the user works in engineering, it can also provide scenarios for technical explanations and problem solving. Furthermore, by providing scenarios specialized for the user's industry, more practical conversation practice becomes possible. This makes it possible to provide scenarios customized according to the user's occupation and industry.

[0124] The reception unit can estimate the user's emotions and adjust the timing of receiving the pronunciation based on the estimated user's emotions. For example, if the user is nervous, the reception unit can wait a short time to allow the user to relax before receiving the pronunciation. Also, if the user is relaxed, the reception unit can immediately receive the pronunciation. Furthermore, if the user is impatient, the reception unit can quickly receive the pronunciation. This makes it possible to adjust the timing of receiving the pronunciation according to the user's emotions.

[0125] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user emotions. For example, if the user is nervous, the accuracy of voice recognition can be increased to reduce recognition errors. Also, if the user is relaxed, the accuracy of voice recognition can be returned to normal. Furthermore, if the user is impatient, the accuracy of voice recognition can be increased to perform analysis more quickly. In this way, by adjusting the accuracy of voice recognition according to the user's emotions, it is possible to reduce recognition errors.

[0126] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is nervous, the evaluation criteria can be relaxed. Also, if the user is relaxed, the normal evaluation criteria can be applied. Furthermore, if the user is impatient, the evaluation criteria can be relaxed. In this way, by adjusting the evaluation criteria according to the user's emotions, more appropriate evaluation can be performed.

[0127] The feedback unit can estimate the user's emotions and adjust the way in which feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is impatient, the feedback unit can provide concise feedback. This allows for more appropriate feedback by adjusting the way in which feedback is expressed depending on the user's emotions.

[0128] The scenario unit can estimate the user's emotions and adjust the display method of the scenario based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to display a more appropriate scenario by adjusting the display method of the scenario according to the user's emotions.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit receives pronunciation from the user. For example, the words pronounced by the user are received by a microphone and stored as voice data. Step 2: The speech recognition unit analyzes the pronunciation received by the reception unit. For example, the speech recognition unit analyzes the speech data using speech recognition technology and evaluates accuracy at the phoneme level. Step 3: The evaluation unit evaluates the accuracy of the pronunciation analyzed by the speech recognition unit. For example, the evaluation unit may evaluate based on the degree of phoneme agreement and the timing of pronunciation. Step 4: The feedback unit feeds back the results of the evaluation by the evaluation unit. For example, the feedback unit provides the evaluation results to the user visually and audibly. Step 5: The scenario module provides conversations based on business scenarios. For example, the scenario module provides business scenarios such as meeting progress, presentations, and negotiations, allowing users to practice conversations based on actual business situations.

[0131] 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.

[0132] 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.

[0133] 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.

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

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 a pronunciation from a user; a speech recognition unit that analyzes the pronunciation accepted by the accepting unit; an evaluation unit that evaluates the accuracy of the pronunciation analyzed by the speech recognition unit; a feedback unit that feeds back the results of the evaluation by the evaluation unit; A scenario unit that provides a conversation based on a business scenario. A system characterized by:

2. The voice recognition unit Analyze user pronunciation in real time 2. The system of claim 1.

3. The evaluation unit Evaluate pronunciation based on accuracy at the phoneme level 2. The system of claim 1.

4. The feedback unit Visual and audio feedback of evaluation results 2. The system of claim 1.

5. The scenario section Provides business scenarios such as meeting management, presentations, and negotiations 2. The system of claim 1.

6. The reception unit Estimate the user's emotion and adjust the timing of accepting pronunciation based on the estimated user's emotion.

2. The system of claim 1.

7. The reception unit Analyze the user's past pronunciation history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When receiving pronunciations, filter them based on the user's current learning progress and areas of interest.

2. The system of claim 1.

Images