Information processing method, program, and information processing apparatus

Abstract

To provide an information processing method or the like capable of appropriately determining whether or not it is necessary to intervene in utterance of a learner.SOLUTION: The information processing method includes acquiring utterance data of a learner in a learning language, detecting a stumbling point from the utterance data, estimating, when the stumbling point is detected, a degree of fixation of a learning item corresponding to the stumbling point and a certainty factor indicating certainty of the degree of fixation from the utterance data, and determining whether or not it is necessary to intervene in the utterance of the learner based on the certainty factor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing method, a program, and an information processing device. [Background technology]

[0002] Various dialogue systems for language learning have been proposed. For example, Patent Document 1 discloses an information processing method in which a question at one of a plurality of levels is output (uttered), an answer to the question is received from the interlocutor (learner), the level is gradually raised and questions are output one after another, and when a breakdown such as a lack of understanding of the content or disfluency is detected from the answer, the level of the question at the time the breakdown was detected is determined to be the level of the interlocutor. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-142373 Summary of the Invention [Problem to be solved by the invention]

[0004] In one aspect, an object of the present invention is to provide an information processing method etc. that can appropriately determine whether or not it is necessary to intervene in a learner's speech. [Means for solving the problem]

[0005] The information processing method involves acquiring speech data in a language being learned by a learner, detecting stumbling blocks from the speech data, and, if a stumbling block is detected, estimating from the speech data the degree of mastery of the learning item corresponding to the stumbling block and a confidence level indicating the likelihood of that mastery, and having the computer execute a process to determine whether or not it is necessary to intervene in the learner's speech based on the confidence level. [Effects of the Invention]

[0006] In one aspect, it is possible to appropriately determine whether or not it is necessary to intervene in the learner's speech. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a dialogue system. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of a server. [Figure 3] 10A and 10B are diagrams illustrating examples of record layouts of a learner DB and a learning item DB. [Figure 4] FIG. 1 is a diagram illustrating an overview of an embodiment. [Figure 5] 10 is a flowchart illustrating an example of a processing procedure executed by a server. DETAILED DESCRIPTION OF THE INVENTION

[0008] The present invention will be described in detail below with reference to the drawings showing embodiments thereof. (Embodiment) Fig. 1 is a diagram showing an example of the configuration of a dialogue system. In this embodiment, a dialogue system is described that engages in dialogue with a learner in a learning language, and that, when a predetermined stumbling point is detected in the learner's speech, determines whether or not intervention is necessary in the learner's speech and provides feedback to the learner according to the determination result. The dialogue system includes an information processing device 1 and a terminal 2. Each device is communicatively connected via a network N such as the Internet.

[0009] The information processing device 1 is an information processing device capable of various information processing and information transmission and reception, such as a server computer or a personal computer. In this embodiment, the information processing device 1 is assumed to be a server computer, and for simplicity, will be referred to as server 1 below. The server 1 conducts a dialogue with the learner in a learning language (e.g., English) in a question-and-answer format. Specifically, the server 1 outputs question data (voice) to the learner according to a predetermined scenario, and accepts input of speech data (voice) from the learner as a response to the question data.

[0010] In this embodiment, the dialogue with the learner is conducted in a question and answer format, but the dialogue format is not limited to the question and answer format.

[0011] In this embodiment, the server 1 detects points where the learner stumbles from the learner's speech data and performs a process of providing appropriate feedback (e.g., correction of the stumbled points). Specifically, as described below, for each predetermined learning item, the server 1 counts the number of times the learning item was used correctly (the number of times the learner was able to speak without stumble) and the number of times the learner was unable to use the learning item correctly (the number of times the learner stumbled). Based on the counted number, the server 1 estimates the degree of mastery of the learning item corresponding to the stumbled point and a confidence factor indicating the likelihood of that mastery. The server 1 then determines whether or not it is necessary to intervene in the learner's speech based on the confidence factor, and if it determines that intervention is necessary, it generates and outputs intervening utterance data for intervening in the learner's speech.

[0012] The terminal 2 is a terminal device used by the learner, such as a personal computer, a smartphone, a tablet terminal, etc. The server 1 outputs question data to the learner via the terminal 2 and accepts input of utterance data as a response to the question data.

[0013] 2 is a block diagram showing an example of the configuration of the server 1. The server 1 includes a control unit 11, a main memory unit 12, a communication unit 13, and an auxiliary memory unit . The control unit 11 has one or more arithmetic processing devices such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit), etc., and performs various information processing, control processing, etc. by reading and executing a program P stored in the auxiliary storage unit 14. The main storage unit 12 is a temporary storage area such as an SRAM (Static Random Access Memory) or a DRAM (Dynamic Random Access Memory), and temporarily stores data necessary for the control unit 11 to execute arithmetic processing. The communication unit 13 is a communication module for performing communication-related processing, and transmits and receives information to and from the outside.

[0014] The auxiliary storage unit 14 is a non-volatile storage area such as a large-capacity memory or a hard disk, and stores a program P (program product) and other data required for the control unit 11 to execute processing. The auxiliary storage unit 14 also stores a learner DB 141 and a learning item DB 142. The learner DB 141 is a database that stores information about each learner. The learning item DB 142 is a database that stores predetermined learning items and intervention methods (templates of intervention utterance data) for intervening with a learner regarding the learning items.

[0015] The auxiliary storage unit 14 may be an external storage device connected to the server 1. The server 1 may be a multi-computer consisting of multiple computers, or may be a virtual machine virtually constructed by software.

[0016] Furthermore, in this embodiment, the server 1 is not limited to the above configuration, and may include, for example, an input unit that accepts operation input, a display unit that displays images, etc. Furthermore, the server 1 may be provided with a reading unit that reads a portable storage medium 1a such as a CD (Compact Disk)-ROM or a DVD (Digital Versatile Disc)-ROM, and may read and execute the program P from the portable storage medium 1a.

[0017] FIG. 3 shows an example of the record layout of the learner DB 141 and the learning item DB 142. As shown in FIG.

[0018] The learner DB 141 includes a learner ID column, a proficiency column, a learning goal column, a learning item column, an n column, and an N column. The learner ID column stores a learner ID for identifying each learner. The proficiency column, the learning goal column, and the learning item column store the learner's proficiency in the language being studied, learning goal, and learning item, respectively, in association with the learner ID. The proficiency column is data that expresses the learner's language ability using discrete and / or continuous values. In this embodiment, the CEFR (Common European Framework of Reference for Languages) level, which is evaluated using six levels: A1, A2, B1, B2, C1, and C2, is used. In the CEFR, A1 is the lowest level and C2 is the highest level. The n column and the N column store, in association with the learner ID and the learning item, the number of times (n) the target learning item was used correctly, and the total number of times (m) the learning item was used incorrectly (number of samples), N, which is the sum of the number of times (n) the learning item was used correctly and the number of times (m) the learning item was used incorrectly, respectively.

[0019] The learning item DB 142 includes a learning item sequence, a proficiency sequence, a confidence / establishment sequence, and an intervention template sequence. The learning item sequence stores learning items. The proficiency sequence, the confidence / establishment sequence, and the intervention template sequence each store, in association with a learning item, proficiency, confidence, and establishment (defined as "high" if above a threshold, and "low" if below the threshold), as well as templates of intervention utterance data.

[0020] 4 is a diagram showing an outline of the embodiment, and the outline of the embodiment will be described with reference to FIG.

[0021] As already mentioned, the server 1 communicates with the learner in a question-and-answer format. Specifically, the server 1 outputs question data to the terminal 2 according to a predetermined scenario and plays back the voice. The server 1 accepts input of speech data (voice) from the learner as a response to the question data.

[0022] The server 1 performs speech recognition on the input speech data, converts the speech data from speech to text, and detects stumbling points from the speech data (text and / or speech).

[0023] A stumbling block refers to an undesirable part of an utterance, such as a grammatical error, a long silence, a pronunciation error, an inconsistent discourse structure (the order of sentences or clauses, the use of conjunctions), etc. In this embodiment, as an example, grammatical errors are detected as stumbling blocks from utterance data (text).

[0024] For example, the server 1 detects stumbling points using a machine learning model as an example of a means for detecting stumbling points. Specifically, the server 1 detects stumbling points using GECToR (Grammatical Error Correction: Tag, Not Rewrite). GECToR is a model that identifies stumbling points (grammatical errors) in an input sentence as a sequence labeling problem, and outputs a tag indicating the type of stumbling point along with a sentence corrected for the stumbling point. For example, if the sentence "I go to the store yesterday." is input, the server 1 outputs the correction result "I went to the store yesterday." and the tag "$VERB_FORM_VB_VBD" (to change to past tense) as a tag indicating the operation of correcting "go" to "went."

[0025] In this way, when utterance data is input, the server 1 detects the stumbling block using a machine learning model (correction model) that outputs data that corrects the stumbling block in the utterance data, i.e., corrected utterance data, and a tag that indicates the type of stumbling block. For example, if the utterance data acquired from the terminal 2 differs from the corrected utterance data output from the model, the server 1 detects the difference as the stumbling block.

[0026] It should be noted that the server 1 only needs to be able to appropriately detect stumbling points from the speech data, and the means for detection is not limited to the machine learning model (GECToR) described above.

[0027] Next, when the server 1 detects a stumbling block from the speech data, it determines whether or not it is necessary to intervene in the learner's speech based on the detection result of the stumbling block, etc. Specifically, the server 1 identifies one or more learning items included in the speech data, estimates the degree of understanding and confidence of each learning item from the detection result of the stumbling block and the identification result of the learning item, and determines the need for intervention based on the confidence.

[0028] First, the server 1 identifies the learning items included in the speech data. Learning items are key points for a learner to learn the language being studied, and a finite number of learning items are defined in the learning item DB 142 (and the learner DB 141). For example, if the language being studied is English, learning items related to grammar include whether verb conjugations are used correctly, whether the word order and position of each word are correct, etc.

[0029] For example, the server 1 identifies a learning item from the corrected utterance data corrected by the above machine learning model. Specifically, the server 1 performs morphological analysis on the corrected utterance data and identifies the part of speech of each morpheme (e.g., word) that constitutes the utterance data. The server 1 identifies the learning item included in the utterance data based on the part of speech identification result. For example, if the corrected utterance data is "I went to the store yesterday," went: verb (past tense of go) Sentence pattern: SVM (subject, verb, modifier) to: preposition yesterday: adverb The result is used as a query to search the learning item DB 142, and it is determined that the utterance sentence is made up of the following learning items. "Conjugation form: past tense" "Word Order and Position: SVM Sentence Patterns" "Word Order and Position: Preposition Selection" "Word Order and Position: Position of Adverbs"

[0030] Based on the results of detecting stumbling points and identifying learning items, the server 1 counts, for each learning item, the number of times the learning item was used correctly and the number of times the learning item was used incorrectly.The server 1 then estimates the degree of mastery of the learning item corresponding to the stumbling point and a confidence factor indicating the likelihood of that mastery based on the number of times the learning item was used correctly and the number of times the learning item was used incorrectly.

[0031] Specifically, the server 1 estimates the degree of fixation according to the following formula (1).

[0032]

number

[0033] R represents learner u's level of mastery of learning item i, n represents the number of times learner u was able to use learning item i correctly, m represents the number of times learner u was able to use learning item i incorrectly, and N represents the sum of n and m (the number of times learning item i was observed). In this way, mastery represents the percentage of times the target learning item was used correctly out of the number N of times the target learning item was observed during a conversation, including the number of times there were stumbles (number of times grammatical errors were made).

[0034] Since learners are constantly growing, N may not be counted from the time they start using the system, but may instead focus on the most recent phenomena and set an upper limit on the value of N, such as the most recent M observations (e.g., M=10).

[0035] For example, if the learner's utterance data is "I go to the store yesterday," and the corrected utterance data is "I went to the store yesterday," no stumbling blocks are detected for the learning items "word order / position: SVM sentence pattern," "word order / position: preposition selection," and "word order / position: adverb position," so the number of times n each learning item was used correctly is incremented by 1. In contrast, a stumbling block is detected for the learning item "conjugation form: past tense," so the number of times m this learning item was used incorrectly is incremented by 1. In this way, the server 1 sequentially detects stumbling blocks from the learner's utterance data to identify the learning items, and counts the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly.

[0036] For each learner and learning item, the server 1 stores (saves) in the learner DB 141 the number of times n the learning item was used correctly, and the sum N of the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly (see FIG. 3). The server 1 reads each parameter n and N from the learner DB 141 and increments the parameters n and N each time a learning item is observed. The server 1 updates the parameters n and N stored in the learner DB 141, for example, after the dialogue ends.

[0037] The server 1 calculates (estimates) the degree of retention according to formula (1) based on the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly. The server 1 classifies the calculated degree of retention into two values ​​based on a threshold value according to formula (2) below. Note that the classification does not necessarily have to be binary.

[0038]

number

[0039] T Ris the threshold for the degree of mastery of learning item i for learner u. "0" indicates that the learning item has not been mastered, and "1" indicates that the learning item has been mastered. For example, server 1 has a threshold T R is set to 0.7, and if the retention rate is 0.7 or higher, it is considered "mastered," and if it is less than 0.7, it is considered "not mastered."

[0040] This threshold may vary depending on the learner's learning goal. For example, if the learner's learning goal is to acquire everyday English conversation skills, the server 1 may consider some errors to be acceptable and set the threshold at 0.6. Alternatively, the server 1 may set the threshold at 0.8 for a learner whose learning goal is to study abroad or acquire business English conversation skills. Also, different thresholds may be set for each learning item.

[0041] The server 1 estimates the confidence level as a measure of how reliable the confidence level is, along with the retention level. For example, the confidence level is defined as the sum of the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly, i.e., the number of samples N of the learning item. For example, the server 1 classifies the confidence level into two values ​​based on the number of samples N, according to the following formula (3). Note that the classification does not necessarily have to be binary.

[0042]

number

[0043] T C is the threshold of the confidence of learner u for learning item i. "0" indicates low confidence, and "1" indicates high confidence. For example, server 1 has a threshold T C is set to 10 (M or less), and if N is 10 or more, the judgment is considered to be based on a sufficient number of samples, and therefore the certainty is determined to be high. On the other hand, if N is less than 10, the server 1 determines that the number of samples is not sufficient, and therefore the certainty is determined to be low.

[0044] In this way, the server 1 estimates the degree of understanding and the degree of certainty of the learning item based on the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly. Note that this estimation method is just one example, and the present embodiment is not limited to this.

[0045] For example, when utterance data is input, the server 1 may construct a machine learning model (prediction model) that outputs the degree of mastery of each learning item included in the utterance data, and use the model to estimate the degree of mastery and confidence. Specifically, the server 1 generates a model that outputs a probability value between 0 and 1 representing the degree of mastery for training utterance data (stumbling points) using training data labeled with the correct value of the mastery of the learning item. The server 1 outputs a probability value by inputting the learner's utterance data into the model. For example, the server 1 determines that the learner has "mastered" if the output probability value is equal to or greater than a predetermined threshold (e.g., 0.5), and determines that the learner has "not mastered" if the output probability value is less than the threshold.

[0046] The server 1 also estimates the confidence level by determining whether the probability value (fixation level) output from the model falls within a predetermined numerical range. For example, the server 1 sets the numerical range to 0.25 to 0.75, and determines that the confidence level is low if the probability value falls within the numerical range, and high if it does not fall within the numerical range.

[0047] Note that these thresholds are examples only, and the optimal fixation and confidence thresholds may vary depending on the trained model.

[0048] As described above, the server 1 only needs to be able to estimate the degree of acceptance and the degree of certainty from the speech data, and the estimation method is not limited to the method shown in formulas (1) to (3).

[0049] When the server 1 detects a stumbling point, it determines whether or not it is necessary to intervene in the learner's speech based on the confidence level estimated above. Specifically, when the confidence level (number of samples N) is equal to or greater than a threshold and is classified as "1," the server 1 determines that it is necessary to intervene in the learner's speech.

[0050] If it is determined that intervention is necessary, the server 1 generates intervening utterance data (text) for intervening in the learner's utterance, and outputs the voice-synthesized intervening utterance data (voice) to the terminal 2. In this embodiment, templates of intervening utterance data (text) associated with learning items are stored in the learning item DB 142. The server 1 applies the learner's utterance data to the templates to generate intervening utterance data.

[0051] Here, different templates are stored in the learning item DB 142 in association with the degree of understanding (and confidence level) and the level of proficiency (see FIG. 3). The server 1 selects a different template depending on the degree of understanding estimated above and the learner's level of proficiency, and generates intervening utterance data.

[0052] Following the example above, consider the case where the learner's utterance data is "I go to the store yesterday." and the corrected utterance data is "I went to the store yesterday." In this case, "go" (went) is detected as the stumbling block, and "conjugation form: past tense" is identified as the learning item. Here, if the retention level is "low" ("not mastered") and the learner's proficiency level is "A1," the server 1 selects "You should use "x" when you are talking about a past event" as the template. The server 1 generates "You should use "went" when you are talking about a past event" as the intervention utterance data by replacing the variable x with the corrected word "went."

[0053] On the other hand, if the retention level is "high" ("mastered"), the server 1 selects "So you meant 'x'?" as the template. The server 1 generates the intervention utterance data "So you meant 'you went to the store yesterday'?" by replacing the subject of the correction utterance data from "I" to "you" and applying it to the variable x. In this way, the server 1 directly corrects the stumbling block when the retention level is low, but indirectly corrects when the retention level is high, allowing the learner to become aware of the problem.

[0054] Similarly, different templates are stored in the learning item DB 142 according to the learner's level of proficiency, and the server 1 generates intervention utterance data of different difficulty levels according to the learner's level of proficiency. This allows intervention in a way that suits the learner's level.

[0055] In the above, the intervening utterance data is generated rule-based according to a template, but the present embodiment is not limited to this. For example, the server 1 may generate the intervening utterance data using a large-scale language model. In this case, the server 1 prepares prompts associated with learning items, etc. in the learning item DB 142, adds utterance data before and after correction to the prompts, and inputs the utterance data into the large-scale language model to generate the intervening utterance data. This makes it possible to generate a variety of intervening utterance data.

[0056] In this way, when the server 1 determines that the confidence level is high and that intervention in the learner's utterance is necessary, it generates intervention utterance data and outputs it to the terminal 2. On the other hand, when the server 1 detects a stumbling point but determines that the confidence level is low, it generates additional question data (text) related to the target learning item and outputs the synthesized additional question data (audio) to the terminal 2.

[0057] For example, the server 1 generates additional question data by inputting a predetermined prompt into a large-scale language model. For example, if the server 1 detects the stumbling block "go" in the learner's utterance data "I go to the store yesterday." and identifies the learning item "conjugation: past tense," but the confidence level of the learning item's retention is below a threshold, the server 1 adds the corrected utterance data "I went to the store yesterday." and the learning item "conjugation: past tense" to the prompt and inputs them into the large-scale language model to generate additional question data such as "What did you buy there?" By outputting the additional question data to the learner, the server 1 expects the learner to use the learning item "conjugation: past tense." If the learner correctly uses the "past tense" to utter, the retention level increases. Conversely, if the learner fails to correctly use the "past tense," the retention level decreases.

[0058] In this way, by outputting additional question data related to the learning item, the sample number N of the target learning item can be increased, making it possible to appropriately determine whether or not intervention is necessary.

[0059] As described above, according to this embodiment, stumbling points are detected from the learner's speech data, the degree of mastery of the learning item corresponding to the stumbling point and its confidence level are estimated, and it is determined whether or not intervention in the learner's speech is necessary. If it is determined that intervention in the utterance is necessary, intervening utterance data is generated and output, and if it is determined that intervention in the utterance is not necessary, additional question data is generated and output. This makes it possible to provide appropriate feedback to the learner and effectively support their language learning.

[0060] 5 is a flowchart showing an example of a processing procedure executed by the server 1. The processing contents executed by the server 1 will be described with reference to FIG. The control unit 11 of the server 1 outputs question data to the terminal 2 according to a predetermined scenario (step S11). The control unit 11 acquires utterance data in the learning language by the learner from the terminal 2 as a response to the question data (step S12).

[0061] The control unit 11 detects stumbling points from the utterance data acquired in step S11 (step S13). Specifically, the control unit 11 inputs the acquired utterance data into a machine learning model (correction model) that has been trained to output corrected utterance data that corrects the stumbling point and a tag that indicates the type of stumbling point when utterance data is input, thereby outputting the corrected utterance data and the tag. The control unit 11 detects the stumbling points by comparing the output corrected utterance data with the original utterance data.

[0062] The control unit 11 identifies the learning items included in the utterance data acquired in step S12 (step S14). Specifically, the control unit 11 performs morphological analysis on the corrected utterance data output from the machine learning model, and identifies the part of speech and / or conjugation form of each morpheme (word). The control unit 11 identifies the learning items included in the utterance data based on the result of identifying the part of speech and / or conjugation form.

[0063] Based on the results of detecting stumbling blocks in step S13 and the results of identifying learning items in step S14, the control unit 11 counts, for each learning item, the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly (step S15). That is, if the control unit 11 does not detect a stumbling block in the utterance data corresponding to the identified learning item, the control unit 11 increments the number of times n the learning item was used correctly. On the other hand, if the control unit 11 detects a stumbling block in the utterance data corresponding to the identified learning item, the control unit 11 increments the number of times m the learning item was used incorrectly.

[0064] The control unit 11 determines whether a stumbling block has been detected from the speech data acquired in step S12 (step S16). If it determines that a stumbling block has not been detected (S16: NO), the control unit 11 determines whether to end the dialogue (whether all question data has been output according to the scenario) (step S17). If it determines not to end the dialogue (S17: NO), the control unit 11 returns the process to step S11 and outputs the next question data according to the scenario. If it determines to end the dialogue (S17: YES), the control unit 11 updates the parameters (the number of times n the learning item was used correctly, and the number of samples N, which is the sum of the number of times n the learning item was used correctly and the number of times m the learning item was used incorrectly) stored in the learner DB 141 (step S18), and ends the series of processes.

[0065] If it is determined that a stumbling block has been detected from the speech data (S16: YES), the control unit 11 estimates the degree of mastery of the learning item corresponding to the stumbling block and a confidence factor indicating the likelihood of the degree of mastery based on the number n of times the learning item was used correctly and the number m of times the learning item was used incorrectly (step S19). Specifically, the control unit 11 calculates the degree of mastery by dividing the number n of times the learning item was used correctly by the number of samples N, which is the sum of the number n of times the learning item was used correctly and the number m of times the learning item was used incorrectly. The control unit 11 also calculates the number N of samples as the confidence factor.

[0066] The control unit 11 determines whether or not it is necessary to intervene in the learner's utterance based on the confidence level estimated in step S19 (step S20). That is, the control unit 11 determines whether or not the confidence level is equal to or greater than a threshold value.

[0067] If it is determined that intervention is necessary (S20: YES), the control unit 11 generates intervening utterance data for intervening in the learner's utterance and outputs it to the terminal 2 (step S21). Specifically, the control unit 11 selects a different template depending on the degree of understanding estimated in step S19 and the learner's proficiency in the language being studied, and generates the intervening utterance data.

[0068] If it is determined that no intervention is necessary (S20: NO), the control unit 11 generates additional question data related to the learning item and outputs it to the terminal 2 (step S22). For example, the control unit 11 generates the additional question data by inputting the dialogue history (question data and utterance data) so far, the target learning item, etc. into a large-scale language model.

[0069] After executing the process of step S21 or S22, the control unit 11 returns the process to step S12.

[0070] As described above, according to this embodiment, it is possible to appropriately determine whether or not it is necessary to intervene in the learner's speech.

[0071] The embodiments disclosed herein are to be considered as illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0072] The matters described in each embodiment can be combined with each other. Furthermore, the independent claims and dependent claims described in the claims can be combined with each other in any combination, regardless of the reference format. Furthermore, although the claims use a format in which a claim references two or more other claims (multi-claim format), this is not limited to this format. A format in which multiple claims (multi-multi-claims) that reference at least one other multiple claim may also be used. [Explanation of symbols]

[0073] 1. Server (information processing device) 11 Control section 12 Main memory 13 Communications Department 14 Auxiliary storage P Program 141 Learner DB 142 Learning Item DB 2. Terminal

Claims

1. Acquire speech data in the language being studied by the learner, Detecting stumbling points from the speech data; When the stumbling point is detected, the degree of mastery of the learning item corresponding to the stumbling point and the degree of certainty indicating the likelihood of the mastery are estimated from the speech data; Based on the confidence level, it is determined whether or not it is necessary to intervene in the learner's speech. An information processing method in which processing is performed by a computer.

2. If it is determined that intervention is necessary, generating and outputting intervening utterance data for intervening in the learner's utterance; If it is determined that no intervention is necessary, additional question data related to the learning item is generated and output. The information processing method according to claim 1 .

3. If it is determined that intervention is necessary, different intervention utterance data is generated according to the degree of fixation. The information processing method according to claim 2 .

4. acquiring the learner's proficiency in the language being studied; If it is determined that intervention is necessary, different intervention utterance data is generated according to the degree of fixation and the degree of proficiency. The information processing method according to claim 3 .

5. The acquired utterance data is input into a correction model that has been trained to output corrected utterance data in which the stumbling point is corrected when the utterance data is input, thereby outputting the corrected utterance data; The utterance data is compared with the corrected utterance data to detect the stumbling point. The information processing method according to claim 1 .

6. Identifying the learning items included in the speech data; counting, for each learning item, the number of times the learning item was used correctly and the number of times the learning item was used incorrectly based on the result of detecting the stumbling block and the result of identifying the learning item; The degree of retention and the degree of confidence of the learning item corresponding to the stumbling point are estimated based on the number of times the learning item was used correctly and the number of times the learning item was not used correctly. The information processing method according to claim 1 .

7. the speech data is text, Identifying the part of speech or conjugation of each morpheme that constitutes the text; Identifying the learning items contained in the text based on the results of identifying the parts of speech or conjugated forms The information processing method according to claim 6.

8. inputting the acquired utterance data into a prediction model that has been trained to output the degree of mastery of the learning item included in the utterance data when the utterance data is input, and outputting the degree of mastery; The certainty factor is estimated by determining whether the fixation factor falls within a predetermined numerical range. The information processing method according to claim 1 .

9. Acquire speech data in the language being studied by the learner, Detecting stumbling points from the speech data; When the stumbling point is detected, the degree of mastery of the learning item corresponding to the stumbling point and the degree of certainty indicating the likelihood of the mastery are estimated from the speech data; Based on the confidence level, it is determined whether or not it is necessary to intervene in the learner's speech. A program that causes a computer to perform a process.

10. An information processing device including a control unit, The control unit Acquire speech data in the language being studied by the learner, Detecting stumbling points from the speech data; When the stumbling point is detected, the degree of mastery of the learning item corresponding to the stumbling point and the degree of certainty indicating the likelihood of the mastery are estimated from the speech data; Based on the confidence level, it is determined whether or not it is necessary to intervene in the learner's speech. Information processing device.

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