system

Abstract

To provide a system capable of identifying speaking habits and pronunciation weaknesses and giving effective feedback.SOLUTION: A system in an embodiment includes a conversion unit, an analysis unit, a generation unit, and an audio generation unit. The conversion unit converts speech to text. The analysis unit analyzes the text converted by the conversion unit and identifies speaking habits and pronunciation weaknesses. The generation unit generates a report based on the analysis results obtained by the analysis unit. The audio generation unit generates sample audio generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques fall short in efficiently identifying speaking habits and pronunciation weaknesses and providing feedback for improvement.

[0005] The system according to the embodiment aims to identify speaking habits and weak points in pronunciation and provide effective feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversion unit, an analysis unit, a generation unit, and a voice generation unit. The conversion unit converts voice into text. The analysis unit analyzes the text converted by the conversion unit and identifies speaking habits and weak points in pronunciation. The generation unit generates a report based on the analysis results obtained by the analysis unit. The voice generation unit generates the model voice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify speaking habits and pronunciation weaknesses and provide effective 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) An English speaking improvement system according to an embodiment of the present invention records a user's voice during a dialogue or a solo conversation, analyzes it using a generation AI, identifies the user's speaking habits and pronunciation weaknesses, and suggests alternative phrases and areas for improvement. This system converts the user's recorded voice into text, and the generation AI analyzes it to identify the user's speaking habits and pronunciation weaknesses and suggest alternative phrases. For example, if a user says, "The weather is nice today," the voice is converted into text, and the generation AI suggests an alternative phrase such as, "It's sunny and pleasant today." The generation AI also generates a report based on the analysis results and provides it to the user. This report includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. For example, it may provide specific advice such as, "You tend to use 'desu' too often. Using 'desu' instead will make your expression more natural." The generation AI then generates a model voice and transmits it to the user via a voice output device. This allows the user to obtain specific references for improving their pronunciation and speaking style. For example, by listening to a sample voice saying, "It's sunny and pleasant today," a user can correct their own pronunciation. This system allows the user to identify weaknesses in their speaking style and pronunciation and learn specific ways to improve. This will improve practical English speaking skills and enable more natural English conversation. This allows the English speaking improvement system to identify weaknesses in the user's speaking style and pronunciation and provide specific ways to improve.

[0029] An English speaking improvement system according to an embodiment includes a conversion unit, an analysis unit, a generation unit, and a speech generation unit. The conversion unit converts speech recorded by a user into text. For example, the conversion unit converts speech into text using speech recognition technology. Examples of speech recognition technology include deep learning-based speech recognition and HMM (hidden Markov model)-based speech recognition. The analysis unit uses a generation AI to analyze the text converted by the conversion unit and identify speaking habits and pronunciation weaknesses. For example, the analysis unit analyzes the text using natural language processing technology to identify speaking habits such as speech rate, intonation, and rhythm, and pronunciation weaknesses such as the pronunciation of specific sounds and accent placement. The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for different situations, such as business settings and casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report including the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. The voice generation unit generates a model voice generated by the generation unit. For example, the voice generation unit may cooperate with a voice output device such as a smartphone or smart speaker to transmit the generated model voice to the user. This allows the user to obtain specific references for improving their pronunciation and speaking style. As a result, the English speaking improvement system according to the embodiment can identify the user's speaking style and pronunciation weaknesses and provide specific methods for improvement.

[0030] The conversion unit converts the user's recorded speech into text. For example, the conversion unit converts speech into text using speech recognition technology. Speech recognition technologies include deep learning-based speech recognition and HMM (Hidden Markov Model)-based speech recognition. Specifically, deep learning-based speech recognition uses neural networks to analyze speech data and recognize phonemes and words with high accuracy. This allows the user's speech to be accurately converted into text. HMM-based speech recognition converts speech into text by modeling temporal variations in speech and probabilistically analyzing the speech signal. By combining these technologies, the conversion unit can convert speech from various environments and speakers into text with high accuracy. Furthermore, the conversion unit can use noise reduction technology to remove background noise and improve the accuracy of speech recognition. For example, even if the user records speech in a noisy environment, noise reduction technology can obtain clear speech data and achieve accurate text conversion. This allows the conversion unit to accurately and quickly convert the user's speech into text and provide the data necessary for subsequent analysis and generation processes.

[0031] The analysis unit uses the generation AI to analyze the text converted by the conversion unit and identify speaking habits and pronunciation weaknesses. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speaking speed, intonation, and rhythm, as well as pronunciation weaknesses such as the pronunciation of specific sounds and the placement of accents. Specifically, the generation AI receives the user's text data as input and analyzes their speaking speed and intonation patterns. For example, it detects when the user tends to speak certain words quickly or when their intonation is flat. It also identifies pronunciation weaknesses when specific phonemes or accent placements are inaccurate. For example, it analyzes when the pronunciation of "r" or "l" is unclear or when accents are not placed correctly. Based on this information, the analysis unit can gain a detailed understanding of the user's speaking style and pronunciation patterns and identify areas for improvement. Furthermore, the analysis unit can relatively evaluate the user's speaking style and pronunciation characteristics by comparing them with past data and data from other users. This allows the analysis unit to accurately identify weaknesses in the user's speaking style and pronunciation and provide basic data for proposing specific ways to improve them.

[0032] The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business situations and casual conversations. For example, the generation unit proposes alternative expressions such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report that includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. Specifically, the generation AI considers the user's speaking style and pronunciation characteristics and proposes appropriate alternative expressions and improvement methods. For example, it proposes more formal expressions in business situations and more natural expressions in casual conversations. The generation unit also specifically identifies areas for improvement in the user's speaking style and pronunciation and suggests how to practice. For example, it suggests specific methods for practicing the pronunciation of specific phonemes and improving intonation. Furthermore, the generation unit tracks the user's progress and regularly updates the report to support continuous improvement. This allows the generation unit to effectively support the user's improvement of speaking style and pronunciation, thereby improving their English speaking ability.

[0033] The speech generation unit generates the model speech generated by the generation unit. For example, the speech generation unit works in conjunction with a speech output device such as a smartphone or smart speaker to transmit the generated model speech to the user. Specifically, the speech generation unit generates the model speech based on alternative expressions and improvements suggested by the generation unit. For example, for the phrase "The weather is nice today," the speech generation unit generates model speech with correct intonation and pronunciation: "It's sunny and pleasant today." The speech generation unit uses synthetic speech technology to generate natural and easy-to-listen speech. This provides the user with specific reference for improving their pronunciation and speaking style. Furthermore, the speech generation unit can receive user feedback and continuously improve the quality of the generated speech. For example, if the user finds a particular speech difficult to hear, the speech generation unit can adjust the speech generation algorithm based on the feedback to generate more intelligible speech. The speech generation unit can also provide model speech with adjusted difficulty levels according to the user's progress. This allows the speech generation unit to effectively support the user's improvement of their English speaking ability and promote continuous learning.

[0034] The analysis unit can perform analysis by combining speech recognition technology and natural language processing technology. The analysis unit performs analysis by combining speech recognition technology and natural language processing technology, for example. Speech recognition technology includes deep learning-based speech recognition and HMM (hidden Markov model)-based speech recognition. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit converts speech into text using speech recognition technology and analyzes the text using natural language processing technology. This improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can convert speech into text and analyze the text using an AI model that combines speech recognition technology and natural language processing technology.

[0035] The generation unit can propose different alternative expressions for different situations, such as business situations or casual conversations. The generation unit proposes different alternative expressions for different situations, such as business situations or casual conversations. Business situations include meetings, presentations, negotiations, etc. Casual conversations include everyday conversations, conversations with friends, and casual chats. For example, in a business situation, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today, isn't it?". Similarly, in a casual conversation, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today, isn't it?". This improves the user's ability to express themselves. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can propose alternative expressions using an AI model that proposes different alternative expressions for different situations, such as business situations or casual conversations.

[0036] The voice generation unit can be linked to a voice output device such as a smartphone or a smart speaker. The voice generation unit can be linked to a voice output device such as a smartphone or a smart speaker. Smartphones include iOS devices and Android devices. Smart speakers include Amazon (registered trademark) Echo and Google (registered trademark) Home. For example, the voice generation unit can be linked to a smartphone and transmit the generated voice of the model to a user through the smartphone's speaker. The voice generation unit can also be linked to a smart speaker and transmit the generated voice of the model to a user through the smart speaker's speaker. This allows the user to easily listen to the generated voice of the model. Some or all of the above-described processing in the voice generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the voice generation unit can transmit the generated voice of the model to a user using an AI model that is linked to a voice output device such as a smartphone or a smart speaker.

[0037] The conversion unit can automatically remove background noise from the audio to achieve more accurate text conversion. For example, the conversion unit can automatically remove background noise from the audio to achieve more accurate text conversion. Background noise includes environmental sounds, noise, echoes, and the like. For example, the conversion unit can filter out ambient noise during recording to obtain clear audio data. The conversion unit can also remove noise in a specific frequency band during speech recognition to improve the accuracy of text conversion. Furthermore, the conversion unit can apply noise reduction technology after recording to convert audio data with minimal noise into text. This removes background noise, improving the accuracy of text conversion. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can perform text conversion using an AI model that automatically removes background noise from the audio to achieve more accurate text conversion.

[0038] The conversion unit can dynamically adjust the conversion algorithm according to the user's speaking speed and volume when recording audio. For example, the conversion unit dynamically adjusts the conversion algorithm according to the user's speaking speed and volume when recording audio. The speaking speed includes the number of words and syllables per unit time. The volume includes measurement in decibels (dB) and adjustment of the volume level. For example, when the user speaks quickly, the conversion unit increases the sampling rate of speech recognition to improve the accuracy of text conversion. Furthermore, when the user speaks softly, the conversion unit can increase the sensitivity of speech recognition to perform accurate text conversion. Furthermore, the conversion unit can dynamically adjust parameters of the conversion algorithm according to the user's speaking speed to achieve optimal text conversion. As a result, optimal text conversion is possible by adjusting the conversion algorithm according to the user's speaking speed and volume. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can perform text conversion using an AI model that dynamically adjusts the conversion algorithm according to the user's speaking speed and volume.

[0039] The conversion unit can appropriately convert dialects and regional expressions based on the user's geographical location information when recording audio. For example, the conversion unit appropriately converts dialects and regional expressions by taking into account the user's geographical location information when recording audio. Geographical location information includes location estimation from GPS data and IP addresses. Dialects and regional expressions include Kansai dialect and Tohoku dialect. For example, if the user is in the Kansai region, the conversion unit performs text conversion by taking into account the Kansai dialect. Furthermore, if the user is in New York, the conversion unit can also appropriately convert expressions specific to New York. Furthermore, if the user is in London, the conversion unit can also perform text conversion by taking into account pronunciations and expressions specific to London. In this way, dialects and regional expressions can be appropriately converted by taking into account the geographical location information. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that appropriately converts dialects and regional expressions by taking into account the user's geographical location information when recording audio.

[0040] The conversion unit can analyze the user's social media activity when recording audio and prioritize conversion of related terms and phrases. For example, the conversion unit can analyze the user's social media activity when recording audio and prioritize conversion of related terms and phrases. Social media activity includes posted content, comments, number of likes, etc. For example, the conversion unit prioritizes text conversion of social media terms frequently used by the user. The conversion unit can also prioritize conversion of phrases related to specific topics from the user's social media activity. Furthermore, the conversion unit can analyze the user's social media posts and appropriately convert related terms into text. In this way, by analyzing social media activity, related terms and phrases can be prioritized. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can analyze the user's social media activity when recording audio and perform text conversion using an AI model that prioritizes conversion of related terms and phrases.

[0041] The analysis unit can improve the accuracy of the analysis by referring to the user's past conversation data during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past conversation data during analysis. Past conversation data includes recorded data, text logs, and the like. For example, the analysis unit can identify speaking patterns based on the user's past conversation data and improve the accuracy of the analysis. The analysis unit can also refer to the user's past pronunciation data to more accurately identify pronunciation weaknesses. Furthermore, the analysis unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and reflect this in the analysis. In this way, by referring to the past conversation data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that improves the accuracy of the analysis by referring to the user's past conversation data during analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the level of the language spoken by the user during analysis. For example, the analysis unit applies different analysis algorithms depending on the level of the language spoken by the user during analysis. Language levels include CEFR (Common European Framework of Reference for Languages) levels and TOEFL scores. For example, the analysis unit applies an algorithm that analyzes basic pronunciation and grammar for beginners. The analysis unit can also apply an algorithm that analyzes more advanced pronunciation and grammar for intermediate learners. Furthermore, the analysis unit can apply an algorithm that analyzes native-level pronunciation and grammar for advanced learners. This enables more appropriate analysis by applying an analysis algorithm depending on the user's language level. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the level of the language spoken by the user during analysis.

[0043] The analysis unit can analyze regional pronunciations and expressions based on the user's geographical location information during analysis. For example, the analysis unit analyzes regional pronunciations and expressions taking into account the user's geographical location information during analysis. Geographical location information includes location estimation from GPS data and IP addresses. Regional pronunciations and expressions include Kansai dialect, Tohoku dialect, etc. For example, if the user is in the Kansai region, the analysis unit analyzes Kansai dialect pronunciations and expressions. Furthermore, if the user is in New York, the analysis unit can analyze New York dialect pronunciations and expressions. Furthermore, if the user is in London, the analysis unit can analyze London dialect pronunciations and expressions. In this way, regional pronunciations and expressions can be appropriately analyzed by taking into account the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that analyzes regional pronunciations and expressions taking into account the user's geographical location information during analysis.

[0044] The analysis unit can analyze the user's social media activity during analysis and identify related speaking habits and pronunciation weaknesses. For example, the analysis unit can analyze the user's social media activity during analysis and identify related speaking habits and pronunciation weaknesses. Social media activity includes the content of posts, comments, the number of likes, and the like. For example, the analysis unit can analyze social media terms frequently used by the user to identify speaking habits. The analysis unit can also identify pronunciation weaknesses related to specific topics from the user's social media activity. Furthermore, the analysis unit can analyze the content of the user's social media posts to identify related speaking habits and pronunciation weaknesses. In this way, related speaking habits and pronunciation weaknesses can be identified by analyzing the social media activity. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the user's social media activity during analysis and identifies related speaking habits and pronunciation weaknesses.

[0045] The generation unit can suggest more specific improvements by referring to the user's past conversation data when generating a report. For example, the generation unit can suggest more specific improvements by referring to the user's past conversation data when generating a report. Past conversation data includes audio recordings and text logs. For example, the generation unit can suggest specific pronunciation improvements based on the user's past conversation data. The generation unit can also refer to the user's past pronunciation data to more accurately identify pronunciation weaknesses. Furthermore, the generation unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and suggest improvements. By referring to the past conversation data, more specific improvements can be suggested. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests more specific improvements by referring to the user's past conversation data.

[0046] The generation unit can apply different report formats depending on the user's language level when generating a report. For example, the generation unit can apply different report formats depending on the user's language level when generating a report. Language levels include CEFR (Common European Framework of Reference for Languages) levels and TOEFL scores. For example, the generation unit can generate a report for beginners that includes basic pronunciation and grammar improvements. The generation unit can also generate a report for intermediate learners that includes more advanced pronunciation and grammar improvements. The generation unit can also generate a report for advanced learners that includes native-level pronunciation and grammar improvements. In this way, a more appropriate report is generated by applying a report format depending on the user's language level. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can generate a report using an AI model that applies different report formats depending on the user's language level when generating a report.

[0047] The generation unit can suggest region-specific expressions and improvements based on the user's geographical location information when generating a report. For example, the generation unit can suggest region-specific expressions and improvements by taking the user's geographical location information into account when generating a report. Geographical location information includes location estimation from GPS data and an IP address. Region-specific expressions and improvements include Kansai dialect, Tohoku dialect, and the like. For example, if the user is in the Kansai region, the generation unit can suggest improvements to pronunciation and expressions specific to Kansai dialect. Furthermore, if the user is in New York, the generation unit can suggest improvements to pronunciation and expressions specific to New York. Furthermore, if the user is in London, the generation unit can suggest improvements to pronunciation and expressions specific to London. In this way, region-specific expressions and improvements can be appropriately suggested by taking the geographical location information into account. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests region-specific expressions and improvements by taking the user's geographical location information into account when generating a report.

[0048] The generation unit can analyze the user's social media activity and suggest related improvements when generating a report. For example, the generation unit can analyze the user's social media activity and suggest related improvements when generating a report. Social media activity includes the content of posts, comments, the number of likes, etc. For example, the generation unit can suggest specific improvements based on social media terms frequently used by the user. The generation unit can also suggest pronunciation improvements related to specific topics from the user's social media activity. Furthermore, the generation unit can analyze the content of the user's social media posts and suggest related speaking improvements. In this way, by analyzing social media activity, related improvements can be appropriately suggested. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a report using an AI model that analyzes the user's social media activity and suggests related improvements when generating a report.

[0049] The speech generation unit can refer to the user's past pronunciation data when generating speech to suggest more specific pronunciation improvements. For example, the speech generation unit can refer to the user's past pronunciation data when generating speech to suggest more specific pronunciation improvements. Past pronunciation data includes recorded data, text logs, and the like. For example, the speech generation unit generates model speech including specific pronunciation improvements based on the user's past pronunciation data. The speech generation unit can also refer to the user's past pronunciation data to generate model speech that highlights weak points in pronunciation. Furthermore, the speech generation unit can generate model speech for improving the pronunciation of specific phrases or expressions from the user's past pronunciation data. By referring to the past pronunciation data, more specific pronunciation improvements can be suggested. Some or all of the above-described processing in the speech generation unit may be performed, for example, using AI, or may be performed without AI. For example, the speech generation unit can generate speech using an AI model that refers to the user's past pronunciation data and suggests more specific pronunciation improvements when generating speech.

[0050] The speech generation unit can apply different speech generation algorithms depending on the level of the language spoken by the user when generating speech. For example, the speech generation unit can apply different speech generation algorithms depending on the level of the language spoken by the user when generating speech. Language levels include CEFR (Common European Framework of Reference for Languages) levels and TOEFL scores. For example, the speech generation unit can generate model speech that emphasizes basic pronunciation and grammar for beginners. The speech generation unit can also generate model speech that includes more advanced pronunciation and grammar for intermediate learners. Furthermore, the speech generation unit can generate model speech that includes native-level pronunciation and grammar for advanced learners. In this way, by applying a speech generation algorithm depending on the user's language level, more appropriate model speech can be provided. Some or all of the above-described processing in the speech generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech generation unit can generate speech using an AI model that applies different speech generation algorithms depending on the level of the language spoken by the user when generating speech.

[0051] The speech generation unit can generate model speech including regional pronunciations and expressions by taking into account the user's geographical location information during speech generation. For example, the speech generation unit generates model speech including regional pronunciations and expressions by taking into account the user's geographical location information during speech generation. Geographical location information includes location estimation from GPS data and IP addresses. Regional pronunciations and expressions include Kansai dialect, Tohoku dialect, and the like. For example, if the user is in the Kansai region, the speech generation unit generates model speech including Kansai dialect pronunciations and expressions. Furthermore, if the user is in New York, the speech generation unit can generate model speech including New York pronunciations and expressions. Furthermore, if the user is in London, the speech generation unit can generate model speech including London pronunciations and expressions. In this way, by taking into account the geographical location information, it is possible to provide model speech including regional pronunciations and expressions. Some or all of the above-described processing in the speech generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech generation unit can generate speech using an AI model that generates model speech including regional pronunciations and expressions by taking into account the user's geographical location information during speech generation.

[0052] The voice generation unit can analyze the user's social media activity and generate a related model voice when generating the voice. For example, the voice generation unit can analyze the user's social media activity and generate a related model voice when generating the voice. Social media activity includes the content of posts, comments, the number of likes, etc. For example, the voice generation unit generates a model voice based on social media terms frequently used by the user. The voice generation unit can also generate a model voice related to a specific topic from the user's social media activity. Furthermore, the voice generation unit can analyze the content of the user's social media posts and generate a related model voice. In this way, it is possible to provide a related model voice by analyzing social media activity. Some or all of the above-described processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can generate a voice using an AI model that analyzes the user's social media activity and generates a related model voice when generating the voice.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analyzer can also understand the context of a user's conversation and provide feedback appropriate to the context. For example, if a user records speech at a business meeting, the analyzer can understand the context and provide feedback on business terms and formal expressions. If a user records a casual conversation, the analyzer can understand the context and provide feedback on the use of expressions and slang appropriate for everyday conversation. Furthermore, if a user is talking about a specific topic, the analyzer can provide feedback on technical terms and expressions related to that topic. This allows the user to learn appropriate expressions according to the context.

[0055] The generator can also track the user's learning progress and provide feedback based on that progress. For example, if the user shows a certain level of improvement in a particular pronunciation or expression, the generator can evaluate that progress and provide more advanced feedback as the next step. If the user is struggling with a particular task, the generator can also provide additional practice or advice for that task. Furthermore, the generator can suggest an individually customized learning plan based on the user's learning history. This allows the user to learn effectively at their own pace.

[0056] The speech generation unit can also analyze the user's pronunciation characteristics and generate individually customized model speech. For example, if the user has trouble pronouncing a particular sound, the speech generation unit can generate model speech that focuses on that sound. Also, if the user struggles with a particular accent or intonation, the speech generation unit can generate model speech that emphasizes that part. Furthermore, the speech generation unit can generate individually customized practice speech based on the user's pronunciation characteristics. This allows the user to effectively improve their pronunciation weaknesses.

[0057] The conversion unit can also convert the user's voice data into text in real time and provide instant feedback. For example, if a user uses a specific phrase during a conversation, the conversion unit can convert the phrase into text in real time and instantly suggest alternative expressions or improvements. Also, if the user is giving a presentation, the conversion unit can provide instant feedback on pronunciation and speaking style. Furthermore, the conversion unit allows the user to receive real-time feedback while practicing, allowing them to make instant corrections and improvements. This allows the user to receive effective feedback in real time.

[0058] The generator can also adjust the form of feedback depending on the user's learning style. For example, visual learners can be provided with feedback using diagrams and graphs. Auditory learners can be provided with audio feedback. Furthermore, practical learners can be provided with feedback that includes specific practice tasks and exercises. This allows the user to receive appropriate feedback according to their learning style.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The conversion unit converts the user's recorded speech into text. For example, the conversion unit converts speech into text using speech recognition technology. Speech recognition technology includes deep learning-based speech recognition and HMM (hidden Markov model)-based speech recognition. Step 2: The analysis unit uses the generative AI to analyze the text converted by the conversion unit and identify speaking habits and weak pronunciation points. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speaking speed, intonation, and rhythm, as well as weak pronunciation points such as the pronunciation of specific sounds and the placement of accents. Step 3: The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business settings or casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report that includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. Step 4: The voice generation unit generates the model voice generated by the generation unit. For example, the voice generation unit may work with a voice output device such as a smartphone or smart speaker to transmit the generated model voice to the user. This allows the user to obtain specific references for improving their pronunciation and speaking style.

[0061] (Example 2) An English speaking improvement system according to an embodiment of the present invention records a user's voice during a dialogue or a solo conversation, analyzes it using a generation AI, identifies the user's speaking habits and pronunciation weaknesses, and suggests alternative phrases and areas for improvement. This system converts the user's recorded voice into text, and the generation AI analyzes it to identify the user's speaking habits and pronunciation weaknesses and suggest alternative phrases. For example, if a user says, "The weather is nice today," the voice is converted into text, and the generation AI suggests an alternative phrase such as, "It's sunny and pleasant today." The generation AI also generates a report based on the analysis results and provides it to the user. This report includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. For example, it may provide specific advice such as, "You tend to use 'desu' too often. Using 'desu' instead will make your expression more natural." The generation AI then generates a model voice and transmits it to the user via a voice output device. This allows the user to obtain specific references for improving their pronunciation and speaking style. For example, by listening to a sample voice saying, "It's sunny and pleasant today," a user can correct their own pronunciation. This system allows the user to identify weaknesses in their speaking style and pronunciation and learn specific ways to improve. This will improve practical English speaking skills and enable more natural English conversation. This allows the English speaking improvement system to identify weaknesses in the user's speaking style and pronunciation and provide specific ways to improve.

[0062] An English speaking improvement system according to an embodiment includes a conversion unit, an analysis unit, a generation unit, and a speech generation unit. The conversion unit converts speech recorded by a user into text. For example, the conversion unit converts speech into text using speech recognition technology. Examples of speech recognition technology include deep learning-based speech recognition and HMM (hidden Markov model)-based speech recognition. The analysis unit uses a generation AI to analyze the text converted by the conversion unit and identify speaking habits and pronunciation weaknesses. For example, the analysis unit analyzes the text using natural language processing technology to identify speaking habits such as speech rate, intonation, and rhythm, and pronunciation weaknesses such as the pronunciation of specific sounds and accent placement. The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for different situations, such as business settings and casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report including the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. The voice generation unit generates a model voice generated by the generation unit. For example, the voice generation unit may cooperate with a voice output device such as a smartphone or smart speaker to transmit the generated model voice to the user. This allows the user to obtain specific references for improving their pronunciation and speaking style. As a result, the English speaking improvement system according to the embodiment can identify the user's speaking style and pronunciation weaknesses and provide specific methods for improvement.

[0063] The conversion unit converts the user's recorded speech into text. For example, the conversion unit converts speech into text using speech recognition technology. Speech recognition technologies include deep learning-based speech recognition and HMM (Hidden Markov Model)-based speech recognition. Specifically, deep learning-based speech recognition uses neural networks to analyze speech data and recognize phonemes and words with high accuracy. This allows the user's speech to be accurately converted into text. HMM-based speech recognition converts speech into text by modeling temporal variations in speech and probabilistically analyzing the speech signal. By combining these technologies, the conversion unit can convert speech from various environments and speakers into text with high accuracy. Furthermore, the conversion unit can use noise reduction technology to remove background noise and improve the accuracy of speech recognition. For example, even if the user records speech in a noisy environment, noise reduction technology can obtain clear speech data and achieve accurate text conversion. This allows the conversion unit to accurately and quickly convert the user's speech into text and provide the data necessary for subsequent analysis and generation processes.

[0064] The analysis unit uses the generation AI to analyze the text converted by the conversion unit and identify speaking habits and pronunciation weaknesses. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speaking speed, intonation, and rhythm, as well as pronunciation weaknesses such as the pronunciation of specific sounds and the placement of accents. Specifically, the generation AI receives the user's text data as input and analyzes their speaking speed and intonation patterns. For example, it detects when the user tends to speak certain words quickly or when their intonation is flat. It also identifies pronunciation weaknesses when specific phonemes or accent placements are inaccurate. For example, it analyzes when the pronunciation of "r" or "l" is unclear or when accents are not placed correctly. Based on this information, the analysis unit can gain a detailed understanding of the user's speaking style and pronunciation patterns and identify areas for improvement. Furthermore, the analysis unit can relatively evaluate the user's speaking style and pronunciation characteristics by comparing them with past data and data from other users. This allows the analysis unit to accurately identify weaknesses in the user's speaking style and pronunciation and provide basic data for proposing specific ways to improve them.

[0065] The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business situations and casual conversations. For example, the generation unit proposes alternative expressions such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report that includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. Specifically, the generation AI considers the user's speaking style and pronunciation characteristics and proposes appropriate alternative expressions and improvement methods. For example, it proposes more formal expressions in business situations and more natural expressions in casual conversations. The generation unit also specifically identifies areas for improvement in the user's speaking style and pronunciation and suggests how to practice. For example, it suggests specific methods for practicing the pronunciation of specific phonemes and improving intonation. Furthermore, the generation unit tracks the user's progress and regularly updates the report to support continuous improvement. This allows the generation unit to effectively support the user's improvement of speaking style and pronunciation, thereby improving their English speaking ability.

[0066] The speech generation unit generates the model speech generated by the generation unit. For example, the speech generation unit works in conjunction with a speech output device such as a smartphone or smart speaker to transmit the generated model speech to the user. Specifically, the speech generation unit generates the model speech based on alternative expressions and improvements suggested by the generation unit. For example, for the phrase "The weather is nice today," the speech generation unit generates model speech with correct intonation and pronunciation: "It's sunny and pleasant today." The speech generation unit uses synthetic speech technology to generate natural and easy-to-listen speech. This provides the user with specific reference for improving their pronunciation and speaking style. Furthermore, the speech generation unit can receive user feedback and continuously improve the quality of the generated speech. For example, if the user finds a particular speech difficult to hear, the speech generation unit can adjust the speech generation algorithm based on the feedback to generate more intelligible speech. The speech generation unit can also provide model speech with adjusted difficulty levels according to the user's progress. This allows the speech generation unit to effectively support the user's improvement of their English speaking ability and promote continuous learning.

[0067] The analysis unit can perform analysis by combining speech recognition technology and natural language processing technology. The analysis unit performs analysis by combining speech recognition technology and natural language processing technology, for example. Speech recognition technology includes deep learning-based speech recognition and HMM (hidden Markov model)-based speech recognition. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit converts speech into text using speech recognition technology and analyzes the text using natural language processing technology. This improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can convert speech into text and analyze the text using an AI model that combines speech recognition technology and natural language processing technology.

[0068] The generation unit can propose different alternative expressions for different situations, such as business situations or casual conversations. The generation unit proposes different alternative expressions for different situations, such as business situations or casual conversations. Business situations include meetings, presentations, negotiations, etc. Casual conversations include everyday conversations, conversations with friends, and casual chats. For example, in a business situation, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today, isn't it?". Similarly, in a casual conversation, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today, isn't it?". This improves the user's ability to express themselves. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can propose alternative expressions using an AI model that proposes different alternative expressions for different situations, such as business situations or casual conversations.

[0069] The voice generation unit can be linked to a voice output device such as a smartphone or a smart speaker. The voice generation unit, for example, links to a voice output device such as a smartphone or a smart speaker. Smartphones include iOS devices and Android devices. Smart speakers include Amazon Echo and Google Home. For example, the voice generation unit links to a smartphone and transmits the generated voice of the model to a user through the smartphone's speaker. The voice generation unit also links to a smart speaker and transmits the generated voice of the model to a user through the smart speaker's speaker. This allows the user to easily listen to the generated voice of the model. Some or all of the above-described processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can transmit the generated voice of the model to a user using an AI model that links to a voice output device such as a smartphone or a smart speaker.

[0070] The conversion unit can estimate the user's emotions and adjust the accuracy of the speech-to-text conversion based on the estimated user emotions. The conversion unit, for example, estimates the user's emotions and adjusts the accuracy of the speech-to-text conversion based on the estimated user emotions. User emotions include joy, sadness, anger, etc. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the conversion unit can increase the sensitivity of speech recognition to perform more accurate text conversion. Furthermore, if the user is relaxed, the conversion unit can also perform text conversion with an emphasis on natural speaking. Furthermore, if the user is in a hurry, the conversion unit can use a simplified conversion algorithm to perform text conversion quickly. This allows for more accurate text conversion by adjusting the accuracy of the text conversion according to the user's emotions. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can perform text conversion using an AI model that estimates a user's emotions and adjusts the accuracy of the speech-to-text conversion based on the estimated user's emotions.

[0071] The conversion unit can automatically remove background noise from the audio to achieve more accurate text conversion. For example, the conversion unit can automatically remove background noise from the audio to achieve more accurate text conversion. Background noise includes environmental sounds, noise, echoes, and the like. For example, the conversion unit can filter out ambient noise during recording to obtain clear audio data. The conversion unit can also remove noise in a specific frequency band during speech recognition to improve the accuracy of text conversion. Furthermore, the conversion unit can apply noise reduction technology after recording to convert audio data with minimal noise into text. This removes background noise, improving the accuracy of text conversion. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can perform text conversion using an AI model that automatically removes background noise from the audio to achieve more accurate text conversion.

[0072] The conversion unit can dynamically adjust the conversion algorithm according to the user's speaking speed and volume when recording audio. For example, the conversion unit dynamically adjusts the conversion algorithm according to the user's speaking speed and volume when recording audio. The speaking speed includes the number of words and syllables per unit time. The volume includes measurement in decibels (dB) and adjustment of the volume level. For example, when the user speaks quickly, the conversion unit increases the sampling rate of speech recognition to improve the accuracy of text conversion. Furthermore, when the user speaks softly, the conversion unit can increase the sensitivity of speech recognition to perform accurate text conversion. Furthermore, the conversion unit can dynamically adjust parameters of the conversion algorithm according to the user's speaking speed to achieve optimal text conversion. As a result, optimal text conversion is possible by adjusting the conversion algorithm according to the user's speaking speed and volume. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can perform text conversion using an AI model that dynamically adjusts the conversion algorithm according to the user's speaking speed and volume.

[0073] The conversion unit can estimate the user's emotion and adjust the display method of the converted text based on the estimated user emotion. For example, the conversion unit can estimate the user's emotion and adjust the display method of the converted text based on the estimated user emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the conversion unit can display the text using a simple, highly visible font. If the user is relaxed, the conversion unit can also display the text using a colorful, fun font. Furthermore, if the user is in a hurry, the conversion unit can highlight important parts when displaying the text. This improves legibility by adjusting the display method of the text according to the user's emotion. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without AI. For example, the conversion unit can display the text using an AI model that estimates the user's emotion and adjusts the display method of the converted text based on the estimated user emotion.

[0074] The conversion unit can appropriately convert dialects and regional expressions based on the user's geographical location information when recording audio. For example, the conversion unit appropriately converts dialects and regional expressions by taking into account the user's geographical location information when recording audio. Geographical location information includes location estimation from GPS data and IP addresses. Dialects and regional expressions include Kansai dialect and Tohoku dialect. For example, if the user is in the Kansai region, the conversion unit performs text conversion by taking into account the Kansai dialect. Furthermore, if the user is in New York, the conversion unit can also appropriately convert expressions specific to New York. Furthermore, if the user is in London, the conversion unit can also perform text conversion by taking into account pronunciations and expressions specific to London. In this way, dialects and regional expressions can be appropriately converted by taking into account the geographical location information. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can perform text conversion using an AI model that appropriately converts dialects and regional expressions by taking into account the user's geographical location information when recording audio.

[0075] The conversion unit can analyze the user's social media activity when recording audio and prioritize conversion of related terms and phrases. For example, the conversion unit can analyze the user's social media activity when recording audio and prioritize conversion of related terms and phrases. Social media activity includes posted content, comments, number of likes, etc. For example, the conversion unit prioritizes text conversion of social media terms frequently used by the user. The conversion unit can also prioritize conversion of phrases related to specific topics from the user's social media activity. Furthermore, the conversion unit can analyze the user's social media posts and appropriately convert related terms into text. In this way, by analyzing social media activity, related terms and phrases can be prioritized. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can analyze the user's social media activity when recording audio and perform text conversion using an AI model that prioritizes conversion of related terms and phrases.

[0076] The analysis unit can estimate the user's emotions and identify speaking habits and pronunciation weaknesses based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and identifies speaking habits and pronunciation weaknesses based on the estimated user emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit can identify speaking habits specific to nervousness when the user is nervous. Furthermore, the analysis unit can identify pronunciation weaknesses when the user is relaxed. Furthermore, the analysis unit can identify pronunciation weaknesses when the user is in a hurry when speaking in a hurry. This enables more appropriate analysis by identifying speaking habits and pronunciation weaknesses according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can perform analysis using an AI model that estimates the user's emotions and identifies speaking habits and pronunciation weaknesses based on the estimated user emotions.

[0077] The analysis unit can improve the accuracy of the analysis by referring to the user's past conversation data during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past conversation data during analysis. Past conversation data includes recorded data, text logs, and the like. For example, the analysis unit can identify speaking patterns based on the user's past conversation data and improve the accuracy of the analysis. The analysis unit can also refer to the user's past pronunciation data to more accurately identify pronunciation weaknesses. Furthermore, the analysis unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and reflect this in the analysis. In this way, by referring to the past conversation data, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that improves the accuracy of the analysis by referring to the user's past conversation data during analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the level of the language spoken by the user during analysis. For example, the analysis unit applies different analysis algorithms depending on the level of the language spoken by the user during analysis. Language levels include CEFR (Common European Framework of Reference for Languages) levels and TOEFL scores. For example, the analysis unit applies an algorithm that analyzes basic pronunciation and grammar for beginners. The analysis unit can also apply an algorithm that analyzes more advanced pronunciation and grammar for intermediate learners. Furthermore, the analysis unit can apply an algorithm that analyzes native-level pronunciation and grammar for advanced learners. This enables more appropriate analysis by applying an analysis algorithm depending on the user's language level. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that applies different analysis algorithms depending on the level of the language spoken by the user during analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can display the analysis results using an AI model that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions.

[0080] The analysis unit can analyze regional pronunciations and expressions based on the user's geographical location information during analysis. For example, the analysis unit analyzes regional pronunciations and expressions taking into account the user's geographical location information during analysis. Geographical location information includes location estimation from GPS data and IP addresses. Regional pronunciations and expressions include Kansai dialect, Tohoku dialect, etc. For example, if the user is in the Kansai region, the analysis unit analyzes Kansai dialect pronunciations and expressions. Furthermore, if the user is in New York, the analysis unit can analyze New York dialect pronunciations and expressions. Furthermore, if the user is in London, the analysis unit can analyze London dialect pronunciations and expressions. In this way, regional pronunciations and expressions can be appropriately analyzed by taking into account the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that analyzes regional pronunciations and expressions taking into account the user's geographical location information during analysis.

[0081] The analysis unit can analyze the user's social media activity during analysis and identify related speaking habits and pronunciation weaknesses. For example, the analysis unit can analyze the user's social media activity during analysis and identify related speaking habits and pronunciation weaknesses. Social media activity includes the content of posts, comments, the number of likes, and the like. For example, the analysis unit can analyze social media terms frequently used by the user to identify speaking habits. The analysis unit can also identify pronunciation weaknesses related to specific topics from the user's social media activity. Furthermore, the analysis unit can analyze the content of the user's social media posts to identify related speaking habits and pronunciation weaknesses. In this way, related speaking habits and pronunciation weaknesses can be identified by analyzing the social media activity. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can perform the analysis using an AI model that analyzes the user's social media activity during analysis and identifies related speaking habits and pronunciation weaknesses.

[0082] The generation unit can estimate the user's emotions and adjust the report presentation style based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the report presentation style based on the estimated user emotions. The 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 these examples. For example, if the user is nervous, the generation AI generates a simple, highly visible report. Furthermore, if the user is relaxed, the generation AI can generate a report that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can generate a report that focuses on the main points. This improves readability by adjusting the report presentation style based on the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that estimates the user's emotions and adjusts the report presentation style based on the estimated user emotions.

[0083] The generation unit can suggest more specific improvements by referring to the user's past conversation data when generating a report. For example, the generation unit can suggest more specific improvements by referring to the user's past conversation data when generating a report. Past conversation data includes audio recordings and text logs. For example, the generation unit can suggest specific pronunciation improvements based on the user's past conversation data. The generation unit can also refer to the user's past pronunciation data to more accurately identify pronunciation weaknesses. Furthermore, the generation unit can analyze the frequency of use of specific phrases and expressions from the user's past conversation data and suggest improvements. By referring to the past conversation data, more specific improvements can be suggested. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests more specific improvements by referring to the user's past conversation data.

[0084] The generation unit can apply different report formats depending on the user's language level when generating a report. For example, the generation unit can apply different report formats depending on the user's language level when generating a report. Language levels include CEFR (Common European Framework of Reference for Languages) levels and TOEFL scores. For example, the generation unit can generate a report for beginners that includes basic pronunciation and grammar improvements. The generation unit can also generate a report for intermediate learners that includes more advanced pronunciation and grammar improvements. The generation unit can also generate a report for advanced learners that includes native-level pronunciation and grammar improvements. In this way, a more appropriate report is generated by applying a report format depending on the user's language level. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can generate a report using an AI model that applies different report formats depending on the user's language level when generating a report.

[0085] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the length of the report based on the estimated user 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 these examples. For example, if the user is nervous, the generation AI generates a short, concise report. Also, if the user is relaxed, the generation AI can generate a longer report with detailed explanations. Furthermore, if the user is in a hurry, the generation AI can generate a short report that can be read quickly. This improves readability by adjusting the length of the report according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that estimates the user's emotions and adjusts the length of the report based on the estimated user emotions.

[0086] The generation unit can suggest region-specific expressions and improvements based on the user's geographical location information when generating a report. For example, the generation unit can suggest region-specific expressions and improvements by taking the user's geographical location information into account when generating a report. Geographical location information includes location estimation from GPS data and an IP address. Region-specific expressions and improvements include Kansai dialect, Tohoku dialect, and the like. For example, if the user is in the Kansai region, the generation unit can suggest improvements to pronunciation and expressions specific to Kansai dialect. Furthermore, if the user is in New York, the generation unit can suggest improvements to pronunciation and expressions specific to New York. Furthermore, if the user is in London, the generation unit can suggest improvements to pronunciation and expressions specific to London. In this way, region-specific expressions and improvements can be appropriately suggested by taking the geographical location information into account. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a report using an AI model that suggests region-specific expressions and improvements by taking the user's geographical location information into account when generating a report.

[0087] The generation unit can analyze the user's social media activity and suggest related improvements when generating a report. For example, the generation unit can analyze the user's social media activity and suggest related improvements when generating a report. Social media activity includes the content of posts, comments, the number of likes, etc. For example, the generation unit can suggest specific improvements based on social media terms frequently used by the user. The generation unit can also suggest pronunciation improvements related to specific topics from the user's social media activity. Furthermore, the generation unit can analyze the content of the user's social media posts and suggest related speaking improvements. In this way, by analyzing social media activity, related improvements can be appropriately suggested. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a report using an AI model that analyzes the user's social media activity and suggests related improvements when generating a report.

[0088] The speech generation unit can estimate the user's emotions and adjust the tone and speed of the model speech based on the estimated user emotions. The speech generation unit, for example, estimates the user's emotions and adjusts the tone and speed of the model speech based on the estimated user emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is nervous, the speech generation unit can generate a model speech with a calm tone and slow speech. If the user is relaxed, the speech generation unit can also generate a model speech with a bright tone and natural speed. Furthermore, if the user is in a hurry, the speech generation unit can generate a quick and concise model speech. By adjusting the tone and speed of the model speech according to the user's emotions, a more appropriate model speech can be provided. Some or all of the above-described processing in the speech generation unit may be performed using, for example, AI, or may be performed without AI. For example, the speech generator can generate speech using an AI model that estimates a user's emotions and adjusts the tone and speed of the model speech based on the estimated user emotions.

[0089] The speech generation unit can refer to the user's past pronunciation data when generating speech to suggest more specific pronunciation improvements. For example, the speech generation unit can refer to the user's past pronunciation data when generating speech to suggest more specific pronunciation improvements. Past pronunciation data includes recorded data, text logs, and the like. For example, the speech generation unit generates model speech including specific pronunciation improvements based on the user's past pronunciation data. The speech generation unit can also refer to the user's past pronunciation data to generate model speech that highlights weak points in pronunciation. Furthermore, the speech generation unit can generate model speech for improving the pronunciation of specific phrases or expressions from the user's past pronunciation data. By referring to the past pronunciation data, more specific pronunciation improvements can be suggested. Some or all of the above-described processing in the speech generation unit may be performed, for example, using AI, or may be performed without AI. For example, the speech generation unit can generate speech using an AI model that refers to the user's past pronunciation data and suggests more specific pronunciation improvements when generating speech.

[0090] The speech generation unit can apply different speech generation algorithms depending on the level of the language spoken by the user when generating speech. For example, the speech generation unit can apply different speech generation algorithms depending on the level of the language spoken by the user when generating speech. Language levels include CEFR (Common European Framework of Reference for Languages) levels and TOEFL scores. For example, the speech generation unit can generate model speech that emphasizes basic pronunciation and grammar for beginners. The speech generation unit can also generate model speech that includes more advanced pronunciation and grammar for intermediate learners. Furthermore, the speech generation unit can generate model speech that includes native-level pronunciation and grammar for advanced learners. In this way, by applying a speech generation algorithm depending on the user's language level, more appropriate model speech can be provided. Some or all of the above-described processing in the speech generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech generation unit can generate speech using an AI model that applies different speech generation algorithms depending on the level of the language spoken by the user when generating speech.

[0091] The voice generation unit can estimate the user's emotion and adjust the length of the model voice based on the estimated user emotion. The voice generation unit, for example, estimates the user's emotion and adjusts the length of the model voice based on the estimated user emotion. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the voice generation unit can generate a short, to-the-point model voice when the user is nervous. The voice generation unit can also generate a longer model voice with detailed explanations when the user is relaxed. Furthermore, the voice generation unit can generate a short model voice that can be quickly listened to when the user is in a hurry. This allows the length of the model voice to be adjusted according to the user's emotion, thereby providing a more appropriate model voice. Some or all of the above-described processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can generate voice using an AI model that estimates the user's emotions and adjusts the length of the sample voice based on the estimated user emotions.

[0092] The speech generation unit can generate model speech including regional pronunciations and expressions by taking into account the user's geographical location information during speech generation. For example, the speech generation unit generates model speech including regional pronunciations and expressions by taking into account the user's geographical location information during speech generation. Geographical location information includes location estimation from GPS data and IP addresses. Regional pronunciations and expressions include Kansai dialect, Tohoku dialect, and the like. For example, if the user is in the Kansai region, the speech generation unit generates model speech including Kansai dialect pronunciations and expressions. Furthermore, if the user is in New York, the speech generation unit can generate model speech including New York pronunciations and expressions. Furthermore, if the user is in London, the speech generation unit can generate model speech including London pronunciations and expressions. In this way, by taking into account the geographical location information, it is possible to provide model speech including regional pronunciations and expressions. Some or all of the above-described processing in the speech generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech generation unit can generate speech using an AI model that generates model speech including regional pronunciations and expressions by taking into account the user's geographical location information during speech generation.

[0093] The voice generation unit can analyze the user's social media activity and generate a related model voice when generating the voice. For example, the voice generation unit can analyze the user's social media activity and generate a related model voice when generating the voice. Social media activity includes the content of posts, comments, the number of likes, etc. For example, the voice generation unit generates a model voice based on social media terms frequently used by the user. The voice generation unit can also generate a model voice related to a specific topic from the user's social media activity. Furthermore, the voice generation unit can analyze the content of the user's social media posts and generate a related model voice. In this way, it is possible to provide a related model voice by analyzing social media activity. Some or all of the above-described processing in the voice generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice generation unit can generate a voice using an AI model that analyzes the user's social media activity and generates a related model voice when generating the voice.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The analyzer can also understand the context of a user's conversation and provide feedback appropriate to the context. For example, if a user records speech at a business meeting, the analyzer can understand the context and provide feedback on business terms and formal expressions. If a user records a casual conversation, the analyzer can understand the context and provide feedback on the use of expressions and slang appropriate for everyday conversation. Furthermore, if a user is talking about a specific topic, the analyzer can provide feedback on technical terms and expressions related to that topic. This allows the user to learn appropriate expressions according to the context.

[0096] The generator can also track the user's learning progress and provide feedback based on that progress. For example, if the user shows a certain level of improvement in a particular pronunciation or expression, the generator can evaluate that progress and provide more advanced feedback as the next step. If the user is struggling with a particular task, the generator can also provide additional practice or advice for that task. Furthermore, the generator can suggest an individually customized learning plan based on the user's learning history. This allows the user to learn effectively at their own pace.

[0097] The speech generation unit can also analyze the user's pronunciation characteristics and generate individually customized model speech. For example, if the user has trouble pronouncing a particular sound, the speech generation unit can generate model speech that focuses on that sound. Also, if the user struggles with a particular accent or intonation, the speech generation unit can generate model speech that emphasizes that part. Furthermore, the speech generation unit can generate individually customized practice speech based on the user's pronunciation characteristics. This allows the user to effectively improve their pronunciation weaknesses.

[0098] The conversion unit can also convert the user's voice data into text in real time and provide instant feedback. For example, if a user uses a specific phrase during a conversation, the conversion unit can convert the phrase into text in real time and instantly suggest alternative expressions or improvements. Also, if the user is giving a presentation, the conversion unit can provide instant feedback on pronunciation and speaking style. Furthermore, the conversion unit allows the user to receive real-time feedback while practicing, allowing them to make instant corrections and improvements. This allows the user to receive effective feedback in real time.

[0099] The analysis unit can also estimate the user's emotions and adjust the tone and content of the feedback based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide encouraging feedback in a gentle tone. If the user is relaxed, the analysis unit can provide detailed feedback. Furthermore, if the user is in a hurry, the analysis unit can provide concise and to-the-point feedback. This makes it possible to provide appropriate feedback according to the user's emotions.

[0100] The generation unit can also estimate the user's emotions and adjust the content and format of the report based on the estimated emotions. For example, if the user is nervous, the generation unit can provide a simple, highly visible report. If the user is relaxed, the generation unit can provide a report containing detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a concise report that focuses on the main points. In this way, it is possible to provide an appropriate report according to the user's emotions.

[0101] The voice generation unit can also estimate the user's emotions and adjust the tone and speed of the model voice based on the estimated emotions. For example, if the user is nervous, the voice generation unit can generate a model voice with a calm tone and slow speed. If the user is relaxed, the voice generation unit can also generate a model voice with a bright tone and natural speed. Furthermore, if the user is in a hurry, the voice generation unit can generate a model voice that is quick and concise. This makes it possible to provide an appropriate model voice according to the user's emotions.

[0102] The conversion unit can also estimate the user's emotions and adjust the accuracy of the speech-to-text conversion based on the estimated emotions. For example, if the user is nervous, the conversion unit can increase the sensitivity of speech recognition to perform more accurate text conversion. Alternatively, if the user is relaxed, the conversion unit can prioritize natural speaking style when converting to text. Furthermore, if the user is in a hurry, a simplified conversion algorithm can be used to quickly convert to text. This allows for more accurate text conversion by adjusting the accuracy of the text conversion according to the user's emotions.

[0103] The analysis unit can also estimate the user's emotions and identify speaking habits and pronunciation weaknesses based on the estimated emotions. For example, if the user is nervous, the analysis unit can identify speaking habits that are specific to nervous situations. Also, if the user is relaxed, the analysis unit can identify pronunciation weaknesses in relaxed situations. Furthermore, if the user is in a hurry, the analysis unit can identify pronunciation weaknesses when speaking in a hurry. This allows for more appropriate analysis by identifying speaking habits and pronunciation weaknesses according to the user's emotions.

[0104] The generator can also adjust the form of feedback depending on the user's learning style. For example, visual learners can be provided with feedback using diagrams and graphs. Auditory learners can be provided with audio feedback. Furthermore, practical learners can be provided with feedback that includes specific practice tasks and exercises. This allows the user to receive appropriate feedback according to their learning style.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The conversion unit converts the user's recorded speech into text. For example, the conversion unit converts speech into text using speech recognition technology. Speech recognition technology includes deep learning-based speech recognition and HMM (hidden Markov model)-based speech recognition. Step 2: The analysis unit uses the generative AI to analyze the text converted by the conversion unit and identify speaking habits and weak pronunciation points. For example, the analysis unit uses natural language processing technology to analyze the text and identify speaking habits such as speaking speed, intonation, and rhythm, as well as weak pronunciation points such as the pronunciation of specific sounds and the placement of accents. Step 3: The generation unit generates a report based on the analysis results obtained by the analysis unit. The generation AI proposes different alternative expressions for each situation, such as business settings or casual conversations. For example, the generation unit proposes an alternative expression such as "It's sunny and pleasant today" for the phrase "The weather is nice today." Based on the analysis results, the generation unit generates a report that includes the user's speaking habits and pronunciation weaknesses, alternative phrases, and areas for improvement. Step 4: The voice generation unit generates the model voice generated by the generation unit. For example, the voice generation unit may work with a voice output device such as a smartphone or smart speaker to transmit the generated model voice to the user. This allows the user to obtain specific references for improving their pronunciation and speaking style.

[0107] 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.

[0108] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0109] 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.

[0110] Each of the multiple elements, including the conversion unit, analysis unit, generation unit, and voice generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the smart device 14 and converts voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using a generation AI to identify speaking habits and pronunciation weaknesses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The voice generation unit is realized, for example, by the control unit 46A of the smart device 14 and transmits the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] Each of the multiple elements, including the conversion unit, analysis unit, generation unit, and voice generation unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the conversion unit is implemented by the control unit 46A of the smart glasses 214 and converts voice recorded by the user into text. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using a generation AI to identify speaking habits and pronunciation weaknesses. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The voice generation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and transmits the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] Each of the multiple elements, including the conversion unit, analysis unit, generation unit, and voice generation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the headset-type terminal 314 and converts voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using a generation AI to identify speaking habits and pronunciation weaknesses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The voice generation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and transmits the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] Each of the multiple elements, including the conversion unit, analysis unit, generation unit, and voice generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversion unit is realized by the control unit 46A of the robot 414 and converts voice recorded by the user into text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using a generation AI to identify speaking habits and pronunciation weaknesses. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a report based on the analysis results. The voice generation unit is realized, for example, by the control unit 46A of the robot 414 and transmits the generated model voice to the user. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Appendix 1) a converter for converting speech to text; an analysis unit that analyzes the text converted by the conversion unit and identifies speaking habits and pronunciation weaknesses; a generation unit that generates a report based on the analysis results obtained by the analysis unit; a voice generating unit that generates a voice of the model generated by the generating unit; A system characterized by: (Appendix 2) The analysis unit Combining speech recognition technology and natural language processing technology for analysis 2. The system of claim 1. (Appendix 3) The generation unit Suggest different alternative expressions for different situations, such as business or casual conversation 2. The system of claim 1. (Appendix 4) The voice generation unit Link with audio output devices such as smartphones or smart speakers 2. The system of claim 1. (Appendix 5) The conversion unit Estimate user emotions and adjust speech-to-text conversion accuracy based on the estimated user emotions 2. The system of claim 1. (Appendix 6) The conversion unit Automatically remove background noise from speech for more accurate transcription 2. The system of claim 1. (Appendix 7) The conversion unit When recording audio, the conversion algorithm dynamically adjusts based on the user's speaking rate and volume. 2. The system of claim 1. (Appendix 8) The conversion unit Inferring user sentiment and adjusting the presentation of the converted text based on the estimated user sentiment 2. The system of claim 1. (Appendix 9) The conversion unit When recording audio, it converts dialects and regional expressions appropriately based on the user's geographic location. 2. The system of claim 1. (Appendix 10) The conversion unit When recording audio, it analyzes your social media activity and prioritizes relevant terms and phrases for conversion. 2. The system of claim 1. (Appendix 11) The analysis unit Estimate the user's emotions and identify speaking habits and pronunciation weaknesses based on the estimated user emotions 2. The system of claim 1. (Appendix 12) The analysis unit During analysis, the accuracy of the analysis is improved by referencing the user's past conversation data. 2. The system of claim 1. (Appendix 13) The analysis unit During parsing, different parsing algorithms are applied depending on the user's language level. 2. The system of claim 1. (Appendix 14) The analysis unit Inferring user emotions and adjusting the display method of analysis results based on the estimated user emotions 2. The system of claim 1. (Appendix 15) The analysis unit During analysis, regional pronunciations and expressions are analyzed based on the user's geographic location. 2. The system of claim 1. (Appendix 16) The analysis unit During analysis, the user's social media activity is analyzed to identify relevant speaking habits and pronunciation weaknesses. 2. The system of claim 1. (Appendix 17) The generation unit Infer user sentiment and adjust report presentation based on the inferred sentiment 2. The system of claim 1. (Appendix 18) The generation unit When generating reports, refer to the user's past conversation data to suggest more specific improvements. 2. The system of claim 1. (Appendix 19) The generation unit When generating reports, apply different report formats depending on the user's language level. 2. The system of claim 1. (Appendix 20) The generation unit Infer user sentiment and adjust report length based on the estimated user sentiment 2. The system of claim 1. (Appendix 21) The generation unit When generating reports, suggest localized wording and improvements based on the user's geographic location. 2. The system of claim 1. (Appendix 22) The generation unit When generating reports, analyze users' social media activity and suggest relevant improvements. 2. The system of claim 1. (Appendix 23) The voice generation unit Inferring the user's emotions and adjusting the tone and speed of the voice model based on the estimated user emotions 2. The system of claim 1. (Appendix 24) The voice generation unit When generating speech, the system refers to the user's past pronunciation data and suggests more specific pronunciation improvements. 2. The system of claim 1. (Appendix 25) The voice generation unit When generating speech, different speech generation algorithms are applied depending on the user's level of language. 2. The system of claim 1. (Appendix 26) The voice generation unit Estimate the user's emotion and adjust the length of the sample audio based on the estimated user emotion. 2. The system of claim 1. (Appendix 27) The voice generation unit When generating speech, the system takes into account the user's geographic location information to generate model speech that includes regional pronunciation and expressions. 2. The system of claim 1. (Appendix 28) The voice generation unit When generating speech, the system analyzes the user's social media activity and generates relevant voice samples. 2. The system of claim 1. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

[Claim 1] A conversion unit that converts the voice spoken by a user into text; an analysis unit that inputs the text converted by the conversion unit and the user's past conversation data into the generation AI and instructs the generation AI to analyze the frequency of use of specific phrases or specific expressions, thereby causing the generation AI to identify the user's speaking habits; a generation unit that causes the generation AI to generate text of alternative expressions that improve the speaking habits obtained by the analysis unit; a voice generation unit that generates a synthetic voice that reads out the text of the alternative expression generated by the generation unit as a model voice. A system characterized by:

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