Intelligent interactive hearing training method

Abstract

The invention discloses an intelligent interactive listening training method, and relates to the technical field of English teaching. Comprising the following steps of forming a preliminary hearing ability evaluation report, automatically generating a personalized learning plan, dynamically adjusting the difficulty and the type of hearing training materials, comparing answers or repeated reading contents of students with standard answers, creating various situational hearing training, disassembling the training materials into a plurality of information units, and carrying out information processing on the information units. An information structure chart is automatically generated, a further learning strategy is provided for different weak links, and panoramic hearing review is provided. According to the intelligent interactive listening training method, by combining the dynamic adaptive training content, personalized feedback and situational simulation, a customized learning path can be provided for the trainee, and the difficulty and content of the training material can be adjusted in real time so as to adapt to the personalized requirements of the trainee. Through multi-dimensional student data acquisition and analysis, weak links of students can be accurately identified, and intensified training can be performed in a targeted manner.

Description

Technical Field

[0001] The invention relates to the technical field of English teaching, in particular to an intelligent interactive listening training method. Background Art

[0002] With the acceleration of globalization, English has become an important tool for international communication, and the demand for listening training has become more urgent. Most of the existing English listening training methods rely on traditional classroom teaching and static practice materials. When facing these trainings, students often find it difficult to obtain personalized feedback and real-time learning adaptation. Therefore, intelligent interactive listening training methods came into being. Traditional English listening training methods usually have the following shortcomings: Lack of personalized adjustment: Most traditional methods use fixed training content and difficulty, and fail to make dynamic adjustments based on students' listening level, error type, and reaction speed, resulting in students having difficulty achieving optimal learning results in materials that are too simple or too complex.

[0003] Lag of feedback mechanism: The feedback in existing technologies is usually limited to a single dimension (such as accuracy), lacking multi-angle analysis and comprehensive evaluation of students’ listening performance. For example, it is impossible to adjust the subsequent training content in a timely manner based on multi-dimensional data such as students’ error types and listening reaction time. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent interactive hearing training method to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides an intelligent interactive listening training method, comprising the following steps: S1. Students take a listening test in the system and form a preliminary listening ability assessment report; S2. Based on the preliminary listening ability assessment report, the system takes into account the student's listening weaknesses and their listening level and automatically generates a personalized learning plan; S3. During the learning process, the system dynamically adjusts the difficulty and type of listening training materials according to the students' learning progress and listening feedback; S4. During the learning process, the system uses voice recognition technology to compare the student's answers or repeated readings with the standard answers and provide timely feedback; S5. During the learning process, create a variety of situational listening exercises to systematically improve students' listening skills to a practical level; S6. Break down the listening training materials into multiple information units, automatically generate an information structure diagram, and mark out key information, important sentences, and possible trap information; S7. After a certain period of listening training, the system provides periodic feedback based on the students' listening progress and gives further learning strategies for different weak links; S8. After completing the study plan, the system provides students with a panoramic listening review. During the review, students listen to the previous training content again and understand and summarize it again.

[0006] To further optimize the technical solution, in step S1, the content of the listening test includes aspects of phonetics, vocabulary, and sentence patterns. The system constructs an analysis model, analyzes the error types of students' pronunciation misrecognition and grammatical structure misunderstanding based on the analysis model, and forms a preliminary listening ability assessment report.

[0007] To further optimize the technical solution, the analysis model divides the students' errors into multiple types, including pronunciation misrecognition E1, grammatical structure misunderstanding E2, vocabulary omission E3, and slow speech E4. According to the severity of each error type, they are quantified by weights W1, W2, W3, and W4. At the same time, for each error type, the system evaluates an error score M1, M2, M3, and M4 based on the student's reaction time and accuracy factors.

[0008] To further optimize this technical solution, the analysis model is as follows: ; in, is the total number of errors made by the learner in the listening test; Is the error type The weight of indicates the impact of the error on the student's listening ability; Is the student in the wrong type The error score on the ,quantifies the learner's performance on that error type.

[0009] To further optimize the technical solution, in step S2, the personalized learning plan includes: Plan the learning time, content and objectives of each training module based on the actual needs of the trainees and their short-term and long-term goals; Based on the students' performance in the listening test, the system recommends highly targeted training materials, including multi-speed listening training, listening exercises with different accents, and listening comprehension in different scenarios.

[0010] To further optimize the technical solution, in step S3, the system constructs an intelligent algorithm model to dynamically adapt the training materials and dynamically adjust the difficulty, type and content of the training materials; The intelligent algorithm model sets the student performance index, dynamic adaptation coefficient and model output; Participant performance indicators include: Accuracy : The proportion of correct answers given by students in a certain exercise; Reaction time : The average time required by students to answer questions; Audio Length : The duration of the current audio material; Audio Difficulty : The difficulty of the current audio, including speaking speed, vocabulary complexity, and sentence structure; The dynamic adaptation factors include: Increase the difficulty : The system determines whether to increase the difficulty of training based on the students’ performance; Reduce Difficulty : When students encounter difficulties, the system will reduce the difficulty of training; Model output: Output a new training material recommendation coefficient , representing the recommended difficulty and content of the next training material.

[0011] To further optimize this technical solution, the intelligent algorithm model is as follows: ; in, is the difficulty coefficient of the current training material; function Defined as: ; in, are weight coefficients, which control the influence of different factors respectively; Reflects the error rate of the learners. If the learners’ error rate is low, it means that more challenging materials are needed, so increase the difficulty; It reflects the ratio of the student's reaction time to the audio length. If the student reacts slowly, it means the audio is too complex and the system will reduce the difficulty appropriately.

[0012] To further optimize the technical solution, in step S4, the system marks the student's pronunciation problems and provides detailed improvement suggestions, including training and pronunciation correction for specific phonemes. Through the instant feedback mechanism, the student continuously corrects his or her own pronunciation and listening comprehension errors, and deepens the memory and understanding of the listening content.

[0013] To further optimize the technical solution, in step S5, different life scenarios are simulated to provide challenging listening materials, requiring students to perform listening comprehension in specific situations; A variety of situational listening exercises to test students' understanding of speech and grasp of contextual information; The system tests whether students can understand the meaning and intention of speech based on different backgrounds, cultures, and topics through the design of language scenarios.

[0014] Further optimizing the technical solution, in step S7, the learning strategies include grammar reconstruction training, vocabulary memorization, speech speed adaptation training, listening summary and keyword extraction, and situation simulation training; The applicable scenarios of each learning strategy are: Grammatical reconstruction training: Students frequently make the same grammatical errors in listening training, or have difficulty understanding complex sentences; Vocabulary memorization method: students often miss or mishear certain words during listening, especially when faced with new words or synonym replacements; Speed ​​adaptation training: Students find it difficult to grasp key information in time when faced with audio with a faster speed; Listening summary and keyword extraction: Students cannot grasp the key points in the listening materials, or the information they hear is relatively scattered; Situational simulation training: The students’ listening comprehension ability is at a beginner level.

[0015] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of an intelligent interactive listening training method as described in the first aspect of the present invention are implemented.

[0016] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of an intelligent interactive listening training method as described in the first aspect of the present invention are implemented.

[0017] Compared with the prior art, the present invention provides an intelligent interactive hearing training method, which has the following beneficial effects: This intelligent interactive listening training method, by combining dynamic adaptation of training content, personalized feedback and situational simulation, can provide students with customized learning paths and adjust the difficulty and content of training materials in real time to meet the individual needs of students. In addition, through multi-dimensional student data collection and analysis, it can accurately identify students' weak links and conduct targeted intensive training. This method not only improves learning efficiency, but also increases the fun and challenge of training, thereby greatly improving students' English listening comprehension and application ability in real environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of a flow chart of an intelligent interactive hearing training method proposed by the present invention; Figure 2 A schematic diagram of the flow of an analysis model in an intelligent interactive listening training method proposed by the present invention; Figure 3 This is a schematic diagram of the classification of learning strategies in the intelligent interactive listening training method proposed by the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Embodiment 1: Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, provides an intelligent interactive listening training method, comprising the following steps: S1. Students take a listening test in the system and form a preliminary listening ability assessment report.

[0024] In this embodiment, the content of the listening test includes aspects of phonetics, vocabulary, and sentence patterns. The system constructs an analysis model, analyzes the error types of students' pronunciation misrecognition and grammatical structure misunderstanding based on the analysis model, and forms a preliminary listening ability assessment report. This assessment report not only provides the overall score of the students, but also can be subdivided into the ability performance of each dimension. This result will serve as a benchmark for the next customized training, thereby providing students with different listening levels with tailored learning plans. The purpose of this step is to build a learning path based on personalized needs, so that the students' progress is more targeted.

[0025] The analysis model divides the students' errors into multiple types, including pronunciation misrecognition E1, grammatical structure misunderstanding E2, vocabulary omission E3, and slow speech E4. The errors are quantified by weights W1, W2, W3, and W4 according to the severity of each error type. At the same time, for each error type, the system evaluates an error score M1, M2, M3, and M4 based on the student's reaction time and accuracy.

[0026] The analysis model is as follows: ; in, is the total number of errors made by the learner in the listening test; Is the error type The weight of indicates the impact of the error on the student's listening ability; Is the student in the wrong type The error score on the ,quantifies the learner's performance on that error type.

[0027] Furthermore, for each error type , its error score The calculation method can be further refined as follows: ; in, is the number of errors the learner made on that type of error.

[0028] It is the total number of questions tested with this type of error, reflecting the proportion of this type of error in the total number of questions.

[0029] It is the student's reaction time (or recognition time) on this type of error. Reaction time refers to the time required for students to respond to a specific error type, usually in seconds. The longer the reaction time, the slower the student processes this error type, which may reflect the difficulty in understanding this type. The higher the value, the slower the student processes this error type, which means poorer comprehension ability in this area.

[0030] When used, the model includes: Data Collection: As students complete the initial listening test, the system automatically records the number of occurrences of each error type. , and the total number of test questions for each error and students’ response time .

[0031] Calculate error scores: The system calculates error scores for each error type based on the recorded data , and combined with the weight of the error type To get the student's overall error score .

[0032] Personalized feedback: based on overall error score , the system will generate a detailed error type analysis report for the student. The report will list the error types in which the student performed poorly and provide specific improvement suggestions. For example, if the error score of mispronunciation is high, the system will recommend specific pronunciation training materials to help students strengthen this part of listening training.

[0033] Optimize learning path: Based on the distribution of students' error types, the system will design a personalized learning plan for them. If students have a big problem in not keeping up with E4 in terms of speaking speed, the system will give priority to recommending some listening materials with a moderate speaking speed and gradually increase the speaking speed. If the vocabulary omissions in E3 are more obvious, the system will increase the vocabulary exercises.

[0034] Suppose a student has the following data in a test: Number of mispronunciation errors , this type of error accounts for the total number of test questions , reaction time Second; Number of grammatical structure misunderstanding errors , this type of error accounts for the total number of test questions , reaction time Second; Number of vocabulary omission errors , this type of error accounts for the total number of test questions , reaction time Second; The speaking speed cannot keep up with the number of errors , this type of error accounts for the total number of test questions , reaction time Second.

[0035] Assume that the weight of each error type is as follows: (Mispronunciation is the most critical); (Grammatical structure misunderstanding is more important); (Lexical omissions are relatively important); (You also need to pay attention if you can’t keep up with the speaking speed).

[0036] Then, the students’ error scores are:

[0037]

[0038]

[0039]

[0040] The overall error score is then:

[0041] according to The system will automatically analyze the student's error types and recommend corresponding training content. If the scores of certain error types are high, the system will develop more targeted training plans based on the student's weak links, thereby optimizing the student's learning path.

[0042] S2. Based on the preliminary listening ability assessment report, the system takes into account the student's listening weaknesses and their listening level and automatically generates a personalized learning plan.

[0043] In this embodiment, the personalized learning plan includes: Plan the learning time, content and objectives of each training module based on the actual needs of the trainees and their short-term and long-term goals; Based on the students' performance in the listening test, the system recommends highly targeted training materials, including multi-speed listening training, listening exercises with different accents, and listening comprehension in different scenarios.

[0044] Through interaction with students, the system can flexibly adjust the progress to avoid fatigue or excessive challenges caused by a fixed learning rhythm. Therefore, intelligent learning progress planning can ensure that each learning stage of students is challenging but not too difficult.

[0045] S3. During the learning process, the system dynamically adjusts the difficulty and type of listening training materials based on the students' learning progress and listening feedback.

[0046] In this embodiment, the system constructs an intelligent algorithm model to dynamically adapt the training materials and dynamically adjust the difficulty, type and content of the training materials. For example, if the student performs poorly in listening comprehension of complex sentences, the system will appropriately increase the relevant grammar or context training. If the student performs well, the system will recommend more difficult audio materials. Through this dynamic adaptation method, the training materials always maintain the adaptability of the students' abilities, avoiding the decline in learning effects due to overly simple or complex content.

[0047] The intelligent algorithm model sets the student performance index, dynamic adaptation coefficient and model output; Participant performance indicators include: Accuracy : The proportion of correct answers given by students in a certain exercise; Reaction time : The average time required by students to answer questions; Audio Length : The duration of the current audio material; Audio Difficulty : The difficulty of the current audio, including speaking speed, vocabulary complexity, and sentence structure; The dynamic adaptation factors include: Increase the difficulty : The system determines whether to increase the difficulty of training based on the students’ performance; Reduce Difficulty : When students encounter difficulties, the system will reduce the difficulty of training; Model output: Output a new training material recommendation coefficient , representing the recommended difficulty and content of the next training material.

[0048] The intelligent algorithm model is as follows: ; in, is the difficulty coefficient of the current training material; function Defined as: ; in, are weight coefficients, which control the influence of different factors respectively; Reflects the error rate of the learners. If the learners’ error rate is low, it means that more challenging materials are needed, so increase the difficulty; It reflects the ratio of the student's reaction time to the audio length. If the student reacts slowly, it means the audio is too complex and the system will reduce the difficulty appropriately.

[0049] When used, the model includes: Real-time monitoring of student performance: When students are doing listening training, the intelligent system will collect the students' accuracy in real time , reaction time , Audio Length and audio difficulty , and calculate the dynamic adaptation coefficient based on these data.

[0050] Dynamically adjust the difficulty of training materials: Based on the above data, the system will calculate the new recommended difficulty coefficient according to the formula , and adjust the difficulty of the training materials accordingly. For example, if the student has a low accuracy rate and slow response on a certain type of material, the system will reduce the difficulty of the next training material. If the student performs well on more challenging materials, the system will increase the difficulty of the next material.

[0051] Personalized learning path generation: Based on each student's performance, the intelligent system will generate a personalized learning path that includes not only the training content, but also the adaptation of training difficulty, audio type, and contextual scenarios. The training materials for each stage are carefully selected based on the student's current level to ensure the challenge and accessibility of the learning process.

[0052] Suppose a student is doing listening training, and the system detects the following data at one stage: Accuracy (meaning 60% of the questions were answered correctly); Reaction time Second; Audio Length minute; Audio Difficulty (The audio is of medium difficulty); The difficulty level of the current training material ; Difficulty increase factor (Students perform well); Difficulty reduction factor (Participants had difficulty with a small number of materials).

[0053] Assume the weight coefficient is: , , , .

[0054] Then, the dynamic adaptation function is calculated using the above model formula:

[0055]

[0056] Finally, the difficulty coefficient of the adjusted training material is:

[0057] because Higher than the current difficulty coefficient , the system will recommend more difficult training materials to further improve students’ listening ability.

[0058] S4. During the learning process, the system uses voice recognition technology to compare the student's answers or repeated readings with the standard answers and provide timely feedback. This feedback is not limited to simple right or wrong prompts, but also goes deeper into the analysis of the accuracy of pronunciation, intonation, rhythm, etc.

[0059] In this embodiment, the system marks the student's pronunciation problems and provides detailed improvement suggestions, including training and pronunciation correction for specific phonemes. Through the instant feedback mechanism, the student continuously corrects his or her pronunciation and listening comprehension errors, deepens the memory and understanding of the listening content, thereby improving listening ability while enhancing the accuracy of language output.

[0060] S5. During the learning process, create a variety of situational listening exercises to systematically improve students' listening ability to a level of practical application.

[0061] In this embodiment, different life scenarios (such as airports, restaurants, business meetings, etc.) are simulated to provide challenging listening materials, requiring students to perform listening comprehension in specific situations; A variety of situational listening exercises to test students' understanding of speech and grasp of contextual information; The system tests whether students can understand the meaning and intention of speech based on different backgrounds, cultures, and topics through the design of language scenarios.

[0062] Through situational training, students’ listening skills are improved, and their ability to respond in real communication scenarios is also improved.

[0063] S6. Break down the listening training materials into multiple information units, automatically generate an information structure diagram, mark the key information, important sentences and possible trap information.

[0064] In this embodiment, the information structure diagram helps students understand the logical relationship of information flow. After students finish listening to a section of audio, the system will guide them to review these structured key information and ask them to repeat or summarize the content of the section. This process not only trains students' attention to details, but also helps them grasp the key points of information in a short time, thereby improving the efficiency of listening and understanding.

[0065] S7. After a certain period of listening training, the system provides periodic feedback based on the students' listening progress and gives further learning strategies for different weak links.

[0066] For example, if some students continue to have difficulty understanding certain grammar or vocabulary, the system will suggest that they adopt strategies such as grammar reconstruction training or vocabulary memorization. This feedback is not only a summary of the students' learning achievements, but also a suggestion for optimizing learning methods. Through targeted strategy adjustments, students can more effectively focus resources on solving actual listening difficulties, thereby making more significant progress in the next stage of learning.

[0067] In this embodiment, the learning strategies include grammar reconstruction training, vocabulary memorization, speech speed adaptation training, listening summary and keyword extraction, and situation simulation training; The applicable scenarios of each learning strategy are: Grammatical reconstruction training: Students often make the same grammatical mistakes in listening training, or have difficulty understanding complex sentences; grammatical reconstruction training strategies can help students systematically improve their grammatical comprehension ability. By repeatedly practicing sentences with different grammatical structures, students need to first listen and gradually disassemble the sentence structure to understand the grammatical function of each component.

[0068] Vocabulary memorization method: Students often miss or mishear certain words during the listening process, especially when faced with new words or synonym replacements; deepen vocabulary memory through multi-dimensional and multi-sensory memorization methods, and consciously highlight the application of these words in the listening materials.

[0069] Speed ​​adaptation training: Students find it difficult to grasp key information in time when faced with fast-speech audio; speed adaptation training can help students gradually improve their listening skills and adapt to materials with different speech speeds. Through phased speed adaptation exercises, the audio playback speed is gradually increased, allowing students to process fast speech information more effectively.

[0070] Listening summary and keyword extraction: Students are unable to grasp the key points in the listening materials, or the information they hear is relatively scattered; by constantly practicing summarizing and extracting keywords in listening training, students will learn to quickly grasp the key information, thereby improving their understanding of the overall content.

[0071] Situational simulation training: The students’ listening comprehension ability is at a beginner level. According to the students’ learning progress, simulate conversations and lectures in different scenarios, such as daily conversations, business conversations, news broadcasts, etc., and gradually increase the complexity of the scenarios.

[0072] S8. After completing the study plan, the system provides students with a panoramic listening review. During the review, students listen to the previous training content again and understand and summarize it again.

[0073] In this embodiment, the purpose of this step is to evaluate the student's mastery of the entire learning process, especially whether they can accurately understand and remember the content after a long time. The system will compare and analyze the student's listening performance and give more detailed optimization suggestions. If the student still has shortcomings in some aspects, the system will generate a personalized retraining plan based on the feedback from the review. Through this periodic review and retraining, the student's listening ability is consolidated in the long term, ensuring that the listening level is not only improved in the short term, but can also be maintained and continued to improve.

[0074] Embodiment 2: This embodiment also provides a computer device, which is applicable to a case of an intelligent interactive listening training method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement an intelligent interactive listening training method proposed in the above embodiment.

[0075] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an intelligent interactive hearing training method as proposed in the above embodiment is implemented.

[0076] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0077] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0079] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0080] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent interactive listening training method, characterized in that: The following steps are involved: S1. Students take a listening test in the system and form a preliminary listening ability assessment report; S2. Based on the preliminary listening ability assessment report, the system takes into account the student's listening weaknesses and their listening level and automatically generates a personalized learning plan; S3. During the learning process, the system dynamically adjusts the difficulty and type of listening training materials according to the students' learning progress and listening feedback; S4. During the learning process, the system uses voice recognition technology to compare the student's answers or repeated readings with the standard answers and provide timely feedback; S5. During the learning process, create a variety of situational listening exercises to systematically improve students' listening skills to a practical level; S6. Break down the listening training materials into multiple information units, automatically generate an information structure diagram, and mark out key information, important sentences, and possible trap information; S7. After a certain period of listening training, the system provides periodic feedback based on the student's listening progress and gives further learning strategies for different weak links; S8. After completing the study plan, the system provides students with a panoramic listening review. During the review, students listen to the previous training content again and understand and summarize it again.

2. The intelligent interactive hearing training method according to claim 1, characterized in that: In step S1, the content of the listening test includes aspects of pronunciation, vocabulary, and sentence patterns. The system constructs an analysis model, analyzes the error types of the students based on the analysis model, and forms a preliminary listening ability assessment report.

3. The intelligent interactive hearing training method according to claim 2, characterized in that: The analysis model divides the students' errors into multiple types, including pronunciation misrecognition E1, grammatical structure misunderstanding E2, vocabulary omission E3, and slow speech E4. The errors are quantified by weights W1, W2, W3, and W4 according to the severity of each error type. At the same time, for each error type, the system evaluates an error score M1, M2, M3, and M4 based on the student's reaction time and accuracy.

4. The intelligent interactive hearing training method according to claim 3, characterized in that: The analysis model is as follows: ； in, is the total number of errors made by the learner in the listening test; Is the error type The weight of indicates the impact of the error on the student's listening ability; Is the student in the wrong type The error score on the ,quantifies the learner's performance on that error type.

5. The intelligent interactive hearing training method according to claim 1, characterized in that: In step S2, the personalized learning plan includes: Plan the learning time, content and objectives of each training module based on the actual needs of the trainees and their short-term and long-term goals; Based on the students' performance in the listening test, the system recommends highly targeted training materials, including multi-speed listening training, listening exercises with different accents, and listening comprehension in different scenarios.

6. The intelligent interactive hearing training method according to claim 1, characterized in that: In step S3, the system constructs an intelligent algorithm model to dynamically adapt the training materials and dynamically adjust the difficulty, type and content of the training materials; The intelligent algorithm model sets the student performance index, dynamic adaptation coefficient and model output; Participant performance indicators include: Accuracy : The proportion of correct answers given by students in a certain exercise; Reaction time : The average time required by students to answer questions; Audio Length : The duration of the current audio material; Audio Difficulty : The difficulty of the current audio, including speaking speed, vocabulary complexity, and sentence structure; The dynamic adaptation factors include: Increase the difficulty : The system determines whether to increase the difficulty of training based on the students’ performance; Reduce Difficulty : When students encounter difficulties, the system will reduce the difficulty of training; Model output: Output a new training material recommendation coefficient , representing the recommended difficulty and content of the next training material.

7. The intelligent interactive hearing training method according to claim 6, characterized in that: The intelligent algorithm model is as follows: ； in, is the difficulty coefficient of the current training material; function Defined as: ； in, are weight coefficients, which control the influence of different factors respectively; Reflects the error rate of the learners. If the learners’ error rate is low, it means that more challenging materials are needed, so increase the difficulty; It reflects the ratio of the student's reaction time to the audio length. If the student reacts slowly, it means the audio is too complex and the system will reduce the difficulty appropriately.

8. The intelligent interactive hearing training method according to claim 1, characterized in that: In step S4, the system marks the student's pronunciation problems and provides detailed improvement suggestions, including training and pronunciation correction for specific phonemes. Through the instant feedback mechanism, the student continuously corrects his or her pronunciation and listening comprehension errors, and deepens the memory and understanding of the listening content.

9. The intelligent interactive hearing training method according to claim 1, characterized in that: In step S5, different life scenarios are simulated to provide challenging listening materials, requiring students to perform listening comprehension in specific situations; A variety of situational listening exercises to test students' understanding of speech and grasp of contextual information; The system tests whether students can understand the meaning and intention of speech based on different backgrounds, cultures, and topics through the design of language scenarios.

10. The intelligent interactive hearing training method according to claim 1, characterized in that: In step S7, the learning strategies include grammar reconstruction training, vocabulary memorization, speech speed adaptation training, listening summary and keyword extraction, and situation simulation training; The applicable scenarios of each learning strategy are: Grammatical reconstruction training: Students frequently make the same grammatical errors in listening training, or have difficulty understanding complex sentences; Vocabulary memorization method: students often miss or mishear certain words during listening, especially when faced with new words or synonym replacements; Speed ​​adaptation training: Students find it difficult to grasp key information in time when faced with audio with a faster speed; Listening summary and keyword extraction: Students cannot grasp the key points in the listening materials, or the information they hear is relatively scattered; Situational simulation training: The students’ listening comprehension ability is at a beginner level.

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