Language learning auxiliary application system based on speech recognition

Abstract

The invention discloses a language learning assistance application system based on speech recognition, and belongs to the technical field of learning assistance. The system comprises an intelligent voice acquisition and enhancement module for realizing acquisition and preprocessing of high-quality voice signals in a complex environment; the multi-modal feature extraction module dynamically extracts and fuses multi-dimensional learning feature information, completes feature analysis and boundary segmentation of a phoneme level, and is responsible for feature normalization and optimization; the deep speech recognition module is used for realizing high-precision speech recognition based on the normalized features; the intelligent evaluation engine module is used for realizing real-time and multi-dimensional evaluation on the language performance of the learner; the personalized learning management module integrates the evaluation results for dynamically planning an optimal learning path for the learner, manages the learning progress, and provides learning effect prediction and intervention suggestions at the same time; and the learning interaction experience module is responsible for visual presentation of learning feedback, multi-modal man-machine interaction design and user interface optimization.

Description

Technical Field

[0001] This application relates to the field of learning assistance technologies, and more specifically, to a language learning assistance application system based on speech recognition. Background Art

[0002] With the continuous development of information technology, intelligent learning tools have gradually become an important part of the education field. In language learning, traditional teaching methods can no longer fully meet the increasingly diverse and personalized needs of learners. Therefore, language learning assistance application systems based on speech recognition have gradually become a trend. Such application systems can effectively improve learners' oral expression abilities and help them enhance their practical language application abilities by combining speech recognition technology with language learning.

[0003] Currently, there are already some speech recognition-assisted learning tools on the market. However, most of these tools have the following problems: First, the accuracy of speech recognition is still relatively low, especially in environments with high ambient noise, and non-standard pronunciations cannot be effectively recognized, resulting in limited learning effects; second, existing application systems mostly have single functions, such as speech input or oral error correction, lacking a systematic language learning path and multi-dimensional learning feedback; in addition, most existing systems ignore the personalized needs of learners and cannot automatically adjust learning content and difficulty according to learners' learning progress and levels, resulting in low learning efficiency.

[0004] In summary, how to improve the accuracy of speech recognition, combine a multi-dimensional learning feedback mechanism, provide a personalized learning path, and ensure the stability and efficiency of the system in different environments has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a language learning assistance application system based on speech recognition for the above problems, including the following modules:

[0006] An intelligent speech acquisition and enhancement module, which realizes the acquisition and preprocessing of high-quality speech signals in complex environments through adaptive noise suppression and multi-channel signal fusion technologies;

[0007] A multi-modal feature extraction module, based on a hierarchical feature extraction architecture, dynamically extracts and fuses multi-dimensional learning feature information, completes phoneme-level feature analysis and boundary segmentation, and is also responsible for feature normalization and optimization;

[0008] A deep speech recognition module, which uses a multi-language hybrid acoustic model and attention-enhanced sequence modeling to achieve high-precision speech recognition based on the normalized features;

[0009] The intelligent evaluation engine module constructs a multi-level scoring standard system to achieve real-time and multi-dimensional evaluation of learners' language performance, and is responsible for generating evaluation results, personalized feedback content, error correction analysis, and optimization suggestions;

[0010] The personalized learning management module, based on a hybrid reasoning mechanism of deep learning and knowledge graph, integrates evaluation results to dynamically plan the optimal learning path for learners and manage the learning progress, and at the same time provides learning effect prediction and intervention suggestions;

[0011] The learning interaction experience module is responsible for the visual presentation of learning feedback, multi-modal human-computer interaction design, and user interface optimization, and displays real-time feedback and suggestions generated by the intelligent evaluation engine to ensure the intuitive and effective transmission of learning guidance and error correction suggestions;

[0012] The system collaborative control module adopts a distributed resource scheduling strategy and an inter-module communication optimization mechanism to coordinate the efficient operation of each functional module and achieve the adaptive optimization of the overall system performance.

[0013] Furthermore, the intelligent voice acquisition and enhancement module includes the following components:

[0014] The adaptive noise suppression unit analyzes the environmental noise in real time and adjusts the noise suppression strategy through an adaptive algorithm to improve the clarity of the voice signal;

[0015] The multi-channel signal fusion unit collects voice signals through multiple microphone arrays and fuses data from different channels to improve the spatial resolution and acquisition accuracy of the signal;

[0016] The signal enhancement and echo cancellation unit integrates signal enhancement and echo cancellation functions, and optimizes the voice quality and removes interference through gain control, dynamic range compression technology, and echo cancellation algorithms;

[0017] The voice activity detection unit accurately identifies the voice signal by detecting the start and end of voice activity, avoiding the influence of irrelevant noise on subsequent processing;

[0018] The signal spectrum reconstruction unit performs spectrum analysis on the voice signal and improves the frequency response range of the voice through a reconstruction algorithm to ensure high-quality voice output;

[0019] The data preprocessing and transmission unit denoises the enhanced voice signal, removes irrelevant information, optimizes the data transmission format, and ensures the effective transmission of the signal to the next-level processing module.

[0020] Furthermore, the multi-modal feature extraction module includes the following components:

[0021] The high-order spectrum feature extraction unit extracts deep frequency domain and time domain features from the preprocessed voice signal to provide audio information for subsequent analysis;

[0022] A video feature extraction unit extracts facial movements and lip movement features in a video signal through visual input to assist in identifying language information in multimodal signals;

[0023] A speech dynamic feature analysis unit analyzes the temporal features of speech signals to provide support for accurate analysis and boundary segmentation at the phoneme level;

[0024] A phoneme analysis and alignment unit combines the temporal features at the phoneme level and the basic phoneme alignment information to accurately analyze the temporal features of each phoneme and perform boundary segmentation to ensure the accuracy of speech recognition;

[0025] A feature fusion and enhancement unit dynamically fuses data from audio, video, and other sensors, enhances the complementarity of different modality information through a deep learning model, and improves the effectiveness of multi-dimensional features;

[0026] A feature normalization and optimization unit performs standardization processing on the extracted features, selects the most representative and discriminative features according to the requirements of the speech recognition task, reduces redundant information, and improves the recognition efficiency and accuracy.

[0027] Furthermore, the deep speech recognition module includes the following components:

[0028] A context-aware feature adaptation unit performs context-related feature transformation on the normalized features to adapt the model input;

[0029] A multilingual hybrid acoustic unit realizes unified modeling and accurate recognition of cross-language phonemes through a hybrid architecture of a shared phoneme mapping layer and a language-specific classifier;

[0030] An attention-enhanced encoder unit adopts a multi-head self-attention mechanism to capture long-range speech temporal dependencies and strengthen the context-related modeling of pronunciation ambiguous segments;

[0031] A semantic alignment decoder unit dynamically fuses acoustic and language features based on attention weights, performs semantic-level alignment on the basis of basic phoneme alignment, generates a text sequence frame by frame, and fuses the semantic coherence of the dialogue scene;

[0032] A language model fusion unit integrates a neural language model and improves the recognition accuracy of proper nouns and complex grammar through a shallow fusion strategy;

[0033] A dynamic vocabulary prediction unit dynamically adjusts the probability distribution of the output vocabulary according to the real-time input language and domain context to adapt to diverse application scenarios;

[0034] A post-processing optimization unit performs punctuation restoration, digital format standardization, and confidence filtering on the recognition results to improve the output readability and system robustness.

[0035] Furthermore, the intelligent evaluation engine module includes the following components:

[0036] The language performance scoring unit analyzes the learner's pronunciation, grammar, and intonation, gives a score based on phoneme and sentence structure features, and evaluates the accuracy of their language performance;

[0037] The speech quality analysis unit combines the quality metadata provided by the signal enhancement and echo cancellation unit, analyzes the clarity, fluency, and stress features of the learner's speech, and provides a quantitative evaluation of the quality of language expression;

[0038] The grammar and semantic understanding unit performs grammar analysis and semantic understanding based on the learner's language input, identifies grammar errors and semantic deviations in the sentences, and ensures the correctness of the language content;

[0039] The progress and performance comparison unit dynamically evaluates the learner's progress by comparing their current performance with their historical learning data, and identifies weak points and areas that need to be strengthened in learning;

[0040] The structured feedback generation unit generates standardized JSON format feedback data based on the scoring and analysis results, providing structured input for subsequent visualization;

[0041] The error correction and optimization suggestion unit provides specific error correction solutions and optimization suggestions for the learner's errors, including pronunciation adjustment, grammar modification, and expression optimization, to help them quickly correct and improve their abilities.

[0042] Furthermore, the personalized learning management module includes the following components:

[0043] The learner profile construction unit constructs a detailed learner profile by analyzing the learner's learning behavior, preferences, and historical performance, providing basic data for personalized recommendation and learning path planning;

[0044] The deep learning inference engine unit uses deep learning algorithms to analyze the learner's learning progress and performance, and adjusts the learning path planning in real time to ensure the personalized adaptation of the learning content;

[0045] The knowledge graph inference unit combines knowledge graph technology, reasons about the relationships and dependencies between knowledge points, and dynamically plans the learner's optimal learning path to ensure the logical coherence and effectiveness of the content;

[0046] The multi-source inference coordinator unit integrates the results of deep learning inference and knowledge graph inference to generate a unified learning path decision;

[0047] The learning progress management unit tracks the learner's learning progress in real time, automatically adjusts the difficulty of tasks and challenges, and ensures that the learning burden is appropriate and matches the learner's capabilities;

[0048] The learning effect prediction unit predicts the learner's future learning effect based on the learner's historical data and current performance, identifies potential learning bottlenecks, and intervenes in advance;

[0049] The intervention suggestion generation unit automatically generates personalized intervention suggestions according to the learning effect prediction results and in combination with the feedback of the evaluation engine, including adjusting the learning plan, recommending supplementary materials, and giving review suggestions, to help learners overcome difficulties;

[0050] The learning plan adjustment unit automatically adjusts the personalized learning plan according to the learner's real-time feedback and performance, optimizes the learning objectives and task arrangements, and improves the learning effect.

[0051] Furthermore, the learning interaction experience module includes the following components:

[0052] The visual feedback display unit receives and parses the structured feedback data generated by the evaluation engine, and is responsible for displaying the learner's learning progress, scores, and feedback, ensuring that learners can intuitively see their learning achievements;

[0053] The voice and text interaction unit combines speech recognition and natural language processing technologies to provide two-way interaction between voice and text, ensuring that learners can interact with the system efficiently through voice or text input;

[0054] The multi-modal feedback fusion unit integrates various feedback forms from the evaluation engine to enhance the learner's immersion and interaction experience, making the feedback more intuitive and vivid;

[0055] The user interface optimization unit continuously optimizes the layout and interaction design of the user interface based on user behavior analysis and feedback, improving the learner's operation efficiency;

[0056] The learning guidance display unit displays personalized learning suggestions and guidance generated based on the evaluation results to help learners optimize their learning strategies, adjust their learning priorities, and improve learning efficiency;

[0057] The interactive guidance and help unit provides real-time guidance and help during the learning process, and uses interactive prompts and virtual assistant functions to help learners solve questions and guide the learning direction.

[0058] Furthermore, the system collaborative control module includes the following components:

[0059] The resource scheduling and management unit dynamically schedules computing resources according to the system load and module requirements, ensuring that each functional module obtains appropriate resource allocation to optimize the system performance and response speed;

[0060] The inter-module communication optimization unit reduces the latency and bandwidth consumption between modules through an efficient communication protocol and data transmission optimization algorithm, ensuring the efficient interconnection of each module and improving the overall system performance;

[0061] The task allocation and priority control unit intelligently allocates computing tasks and adjusts task priorities according to the urgency and complexity of module tasks, ensuring the timely processing of critical tasks and the stable operation of the system;

[0062] The load balancing and fault tolerance mechanism unit monitors the load conditions of each module in the system, dynamically allocates tasks through a load balancing algorithm, and automatically activates the fault tolerance mechanism in case of failures, ensuring the high availability of the system;

[0063] The performance monitoring and feedback unit real-time monitors the performance data of each functional module and adjusts the scheduling strategy through a feedback mechanism to achieve adaptive performance optimization;

[0064] The global optimization decision-making unit analyzes the system operation status from a global perspective, combines machine learning algorithms to automatically generate system performance optimization strategies, ensuring that each module works together to reach the optimal state;

[0065] The adaptive adjustment mechanism unit automatically adjusts the coordination strategy between each module according to system environment and task changes, ensuring that the system continues to operate efficiently under different working conditions and optimizes resource utilization.

[0066] Furthermore, monitoring the load conditions of each module in the system, dynamically allocating tasks through a load balancing algorithm, and automatically activating the fault tolerance mechanism in case of failures includes the following steps:

[0067] Use monitoring tools to collect the resource usage data of each module in real time and set thresholds to trigger alarms;

[0068] Based on the load data, adopt a dynamic load balancing algorithm to adjust task allocation, ensuring the reasonable utilization of resources and avoiding overload;

[0069] When a certain module is overloaded, the system automatically migrates tasks to modules with lighter loads to ensure the uninterrupted completion of tasks;

[0070] When a failure occurs, automatically activate redundant modules to take over tasks to ensure that the system operation is not affected;

[0071] Record the fault log and trigger an alarm to ensure that operation and maintenance personnel can respond and solve problems in a timely manner.

[0072] Furthermore, analyzing the system operation status from a global perspective, combining machine learning algorithms to automatically generate system performance optimization strategies, ensuring that each module works together to reach the optimal state includes the following steps:

[0073] Collect system operation data from each module, including resource usage, task processing speed, and response time, to provide a comprehensive data basis for subsequent analysis;

[0074] Based on the collected data, evaluate the overall health of the system, construct a global state model, analyze the resource dependencies and synergy effects between modules, and identify potential performance bottlenecks and coordination problems;

[0075] Use machine learning algorithms for feature engineering, extract key features crucial for system performance optimization, and perform data cleaning and preprocessing to ensure data quality;

[0076] According to historical performance data and the global state model, train machine learning algorithms to automatically learn and predict which configurations and scheduling strategies can improve system performance;

[0077] Based on the trained machine learning model, automatically generate optimal system performance optimization strategies, and optimize the system state for different workloads and operating environments by adjusting the coordination and resource allocation between modules;

[0078] Apply the generated optimization strategies to the system and monitor their effects in real time. Use the performance feedback mechanism to evaluate the actual effects of the optimization strategies and continuously adjust and optimize according to the feedback.

[0079] Compared with the prior art, the present application has the following beneficial effects:

[0080] The present application realizes efficient speech learning evaluation and dynamic personalized learning path optimization in complex environments by integrating modules such as intelligent voice collection, deep speech recognition, personalized learning management, and system collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic structural diagram of a language learning assistance application system based on speech recognition disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0082] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0083] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0084] The embodiments described below with reference to the accompanying drawings and the directional terms are exemplary and are intended to explain the present invention, and should not be construed as limiting the present invention.

[0085] As Figure 1 shown, a language learning assistance application system based on speech recognition includes the following modules:

[0086] An intelligent speech acquisition and enhancement module, which realizes the acquisition and preprocessing of high-quality speech signals in complex environments through adaptive noise suppression and multi-channel signal fusion technologies;

[0087] A multi-modal feature extraction module, which dynamically extracts and fuses multi-dimensional learning feature information based on a hierarchical feature extraction architecture, completes phoneme-level feature analysis and boundary segmentation, and is also responsible for feature normalization and optimization;

[0088] A deep speech recognition module, which uses a multi-language hybrid acoustic model and attention-enhanced sequence modeling to achieve high-precision speech recognition based on the normalized features;

[0089] An intelligent evaluation engine module, which constructs a multi-level scoring standard system to realize real-time and multi-dimensional evaluation of learners' language performance, and is responsible for generating evaluation results, personalized feedback content, error correction analysis and optimization suggestions;

[0090] A personalized learning management module, which integrates evaluation results based on a hybrid reasoning mechanism of deep learning and knowledge graph to dynamically plan the optimal learning path for learners and manage the learning progress, and at the same time provides learning effect prediction and intervention suggestions;

[0091] A learning interaction experience module, which is responsible for the visual presentation of learning feedback, multi-modal human-computer interaction design and user interface optimization, displays the real-time feedback and suggestions generated by the intelligent evaluation engine, and ensures the intuitive and effective transmission of learning guidance and error correction suggestions;

[0092] A system coordination control module, which adopts a distributed resource scheduling strategy and an inter-module communication optimization mechanism to coordinate the efficient operation of each functional module and realize the adaptive optimization of the overall system performance.

[0093] In this embodiment, the intelligent voice acquisition and enhancement module is the front end of the system, responsible for realizing the acquisition and preprocessing of high-quality voice signals in complex environments. Through adaptive noise suppression technology, this module can dynamically adjust the noise reduction parameters to cope with various background noises, such as street noise, indoor reverberation, and wind noise, etc., ensuring the clarity and integrity of the voice signals. At the same time, the multi-channel signal fusion technology can utilize multiple microphone arrays to enhance the target voice signal through the processing of spatial information and reduce environmental interference. The successful implementation of this module not only improves the accuracy and reliability of voice acquisition but also lays a solid foundation for subsequent feature extraction and speech recognition. In addition, the intelligent voice acquisition and enhancement module also has the ability of self-learning, which can continuously optimize the parameters according to the actual usage situation to improve the adaptability and robustness, enabling it to maintain high-quality voice acquisition effects in different environments.

[0094] In this embodiment, the multi-modal feature extraction module is an important part of the system, aiming to extract and fuse multi-dimensional learning feature information. Based on the hierarchical feature extraction architecture, this module can dynamically analyze various levels of voice signals, including phoneme-level features and high-level semantic information. This hierarchical feature extraction method enables the system to more accurately capture the subtle differences in language and effectively fuse features at different levels. To ensure the normalization and optimization of features, this module also introduces advanced feature standardization technology. By normalizing the feature data, the differences between features are eliminated, and the expression ability and consistency of features are improved. This process not only helps to improve the accuracy of speech recognition but also provides a reliable basis for the multi-dimensional evaluation of the intelligent evaluation engine.

[0095] In this embodiment, the deep speech recognition module uses a multi-language hybrid acoustic model and attention-enhanced sequence modeling technology to achieve high-precision speech recognition. The multi-language hybrid acoustic model enhances the generalization ability of the model by fusing the acoustic features of multiple languages, enabling it to maintain a high recognition accuracy when processing voice inputs in different languages. The attention-enhanced sequence modeling technology, by introducing the attention mechanism, enables the model to better capture the important information in the voice signal, improving the accuracy and robustness of recognition. The normalized features are fully utilized in the deep speech recognition module, enabling the system to still maintain a high recognition rate and processing speed in various complex voice input situations. In addition, this module also has the ability of adaptive learning, which can continuously optimize the model according to the user's voice data to improve the personalized recognition effect.

[0096] In this embodiment, the intelligent evaluation engine module realizes real-time and multi-dimensional evaluation of learners' language performance by constructing a multi-level scoring standard system. This module can not only analyze the basic accuracy and fluency of speech signals, but also comprehensively evaluate multiple dimensions of learners' speech expression, intonation, speech rate rhythm, etc., so as to generate comprehensive evaluation results. Based on these evaluation results, the intelligent evaluation engine can provide personalized feedback content and error correction analysis to help learners identify and improve the deficiencies in language learning. In addition, this module also has the function of generating optimization suggestions, which can put forward targeted optimization suggestions according to the specific situation of learners to help them improve their language ability more efficiently. The real-time evaluation and feedback mechanism enables learners to timely understand their progress and deficiencies during the learning process, which helps to continuously improve and enhance the language learning effect.

[0097] In this embodiment, the personalized learning management module integrates the evaluation results of the intelligent evaluation engine through a hybrid reasoning mechanism of deep learning and knowledge graph, and dynamically plans the optimal learning path and manages the learning progress for learners. Based on the individual differences and learning needs of learners, this module can adjust the learning content and learning plan in real time to ensure the efficiency and personalization of the learning process. At the same time, the personalized learning management module also has the functions of learning effect prediction and intervention suggestion. It can predict the future learning effect of learners according to their learning progress and performance, and put forward targeted intervention suggestions to help learners overcome the bottlenecks in learning and improve learning efficiency. Through refined learning path planning and progress management, this module provides personalized and dynamic learning guidance for learners to ensure continuous progress and optimization in their language learning process.

[0098] In this embodiment, the learning interaction experience module provides intuitive and effective learning feedback and suggestion display for learners through multi-modal human-computer interaction design and user interface optimization. The real-time feedback and optimization suggestions generated by the intelligent evaluation engine are presented in a visual form through the learning interaction experience module, enabling learners to intuitively understand their learning progress and improvement direction. At the same time, this module also pays attention to the design of the human-computer interaction experience. Through multi-modal interaction methods such as voice, touch, and gesture, it improves the interactivity and interest of the learning process. The optimized design of the user interface not only improves the usability and aesthetics of the system, but also enhances the learners' usage experience and learning enthusiasm. Through the learning interaction experience module, learners can more conveniently obtain learning feedback and suggestions, which helps to improve their learning effect and motivation.

[0099] In this embodiment, the system collaborative control module ensures the efficient operation of each functional module and the adaptive optimization of the overall system performance through a distributed resource scheduling strategy and an inter-module communication optimization mechanism. The distributed resource scheduling strategy can dynamically adjust the resource allocation of each module according to the actual load situation of the system, ensuring that the system can still maintain high performance and response speed during high concurrency and complex task processing. At the same time, the inter-module communication optimization mechanism reduces the communication latency and data loss between each module by optimizing the data transmission and processing process, improving the overall collaborative efficiency and reliability of the system. The system collaborative control module also has an adaptive optimization function, which can continuously optimize the parameters and strategies of each module according to the system operation status and user feedback, improving the stability and robustness of the system. Through this module, the system can efficiently coordinate each functional module in complex language learning tasks, ensuring the overall system performance and user experience.

[0100] In summary, the language learning assistance application system based on speech recognition realizes full-process coverage from speech signal acquisition to learning feedback and optimization suggestions through the collaborative work of seven modules: intelligent speech acquisition and enhancement, multi-modal feature extraction, deep speech recognition, intelligent evaluation engine, personalized learning management, learning interaction experience, and system collaborative control. Each module has its own technical characteristics and cooperates with each other to jointly build an efficient, intelligent, and personalized language learning assistance system, providing comprehensive and dynamic learning support and guidance for learners. This system not only improves the efficiency and effect of language learning but also provides important technical references and application demonstrations for the development of the intelligent education field. Through continuous optimization and improvement, the technical effects of each module will be further enhanced, bringing better learning experiences and achievements for learners.

[0101] Furthermore, the intelligent speech acquisition and enhancement module includes the following components:

[0102] An adaptive noise suppression unit that analyzes the ambient noise in real time and adjusts the noise suppression strategy through an adaptive algorithm to improve the clarity of the speech signal;

[0103] A multi-channel signal fusion unit that collects speech signals through multiple microphone arrays and fuses data from different channels to improve the spatial resolution and acquisition accuracy of the signal;

[0104] A signal enhancement and echo cancellation unit that integrates signal enhancement and echo cancellation functions and optimizes the speech quality and removes interference through gain control, dynamic range compression technology, and echo cancellation algorithms;

[0105] A voice activity detection unit that accurately identifies the speech signal by detecting the start and end of voice activities, avoiding the influence of irrelevant noise on subsequent processing;

[0106] A signal spectrum reconstruction unit analyzes the spectrum of the speech signal, improves the frequency response range of the speech through a reconstruction algorithm, and ensures high-quality speech output;

[0107] A data preprocessing and transmission unit performs noise reduction and removes irrelevant information from the enhanced speech signal, optimizes the data transmission format, and ensures the effective transmission of the signal to the next-level processing module.

[0108] In this embodiment, the intelligent speech acquisition and enhancement module realizes high-quality speech acquisition and processing in a complex environment through its six units: adaptive noise suppression, multi-channel signal fusion, signal enhancement and echo cancellation, voice activity detection, signal spectrum reconstruction, and data preprocessing and transmission. The adaptive noise suppression unit improves the clarity of the speech signal by analyzing the environmental noise in real time and adjusting the algorithm; the multi-channel signal fusion unit enhances the spatial resolution and acquisition accuracy of the signal by fusing data from multiple microphone arrays; the signal enhancement and echo cancellation unit optimizes the speech quality and removes interference through various technologies; the voice activity detection unit avoids the influence of irrelevant noise by accurately identifying voice activities; the signal spectrum reconstruction unit improves the frequency response range of the speech through spectrum analysis and reconstruction algorithms, ensuring high-quality speech output; finally, the data preprocessing and transmission unit optimizes the data transmission format by noise reduction and removing irrelevant information, ensuring the effective transmission of the signal. The combined action of these units enables the intelligent speech acquisition and enhancement module to always maintain high-quality speech acquisition and processing effects in complex and changing environments.

[0109] Furthermore, the multi-modal feature extraction module includes the following components:

[0110] A high-order spectrum feature extraction unit extracts deep frequency-domain and time-domain features from the preprocessed speech signal to provide audio information for subsequent analysis;

[0111] A video feature extraction unit extracts facial movement and lip movement features in the video signal through visual input to help identify language information in multi-modal signals;

[0112] A speech dynamic feature analysis unit analyzes the temporal features of the speech signal to provide support for accurate analysis and boundary segmentation at the phoneme level;

[0113] A phoneme analysis and alignment unit combines the temporal features at the phoneme level and the basic phoneme alignment information to accurately analyze the temporal features of each phoneme and perform boundary segmentation to ensure the accuracy of speech recognition;

[0114] A feature fusion and enhancement unit dynamically fuses data from audio, video, and other sensors, and enhances the complementarity of different modal information through a deep learning model to improve the effectiveness of multi-dimensional features;

[0115] The feature normalization and optimization unit performs standardization on the extracted features, selects the most representative and discriminative features according to the requirements of the speech recognition task, reduces redundant information, and improves the recognition efficiency and accuracy.

[0116] In this embodiment, the multi-modal feature extraction module comprehensively extracts and fuses multi-dimensional learning feature information through its six units: high-order spectrum feature extraction, video feature extraction, speech dynamic feature analysis, phoneme analysis and alignment, feature fusion and enhancement, and feature normalization and optimization. The high-order spectrum feature extraction unit extracts deep features from the speech signal. The video feature extraction unit enhances the parsing of language information of the multi-modal signal through the recognition of facial movements and lip features. The speech dynamic feature analysis unit supports accurate analysis and boundary segmentation at the phoneme level through temporal feature analysis. The phoneme analysis and alignment unit combines the temporal features and basic phoneme alignment information to ensure high-precision speech recognition. The feature fusion and enhancement unit enhances the complementarity of different modal information through a deep learning model and improves the effectiveness of the features. The feature normalization and optimization unit selects the most representative and discriminative features through standardization, reduces redundant information, and thus improves the recognition efficiency and accuracy. By integrating these technical means, the multi-modal feature extraction module significantly improves the overall performance and accuracy of the speech recognition system.

[0117] Furthermore, combining the temporal features at the phoneme level and the basic phoneme alignment information, accurately analyzing the temporal features of each phoneme and performing boundary segmentation includes the following steps:

[0118] Determine the start time Start of each phoneme through a phoneme-based alignment tool i and the end time End i ;

[0119] Extract the temporal feature x of each phoneme from the original speech signal x(t) according to the obtained phoneme alignment information i (t);

[0120] Obtain the preliminary boundary of the phoneme by minimizing the error between the temporal feature and the ideal temporal feature x ideal (t), which is expressed by the formula: where Boundary i represents the boundary position of the i-th phoneme;

[0121] Combine the temporal features and energy evaluation to optimize the boundary position, minimize the boundary error and the feature energy difference to obtain the accurate phoneme boundary, which is expressed by the formula: where Cost iThe boundary segmentation cost function representing the phoneme i; λ is the regularization coefficient, used to balance the accuracy and computational complexity of phoneme segmentation, preventing overfitting or overly refined boundaries; E(t) represents the energy function at time t, used to measure the energy level of the speech signal;

[0122] By further comparing the error between the actual time-domain features and the ideal time-domain features of phonemes, and combining the context information of each phoneme, it is ensured that the phoneme segmentation after boundary correction is more accurate, thereby improving the overall accuracy of speech recognition.

[0123] In this embodiment, by combining the analysis of time-domain features at the phoneme level and basic phoneme alignment information, the start and end times of each phoneme can be accurately determined, the time-domain features are extracted from the original speech signal, and the error between the time-domain features and the ideal features is minimized to obtain the preliminary boundary positions. Further combining the time-domain features and energy evaluation to optimize the boundary positions, ensuring high-precision phoneme boundary segmentation, and balancing the segmentation accuracy and computational complexity through the regularization coefficient. Finally, through the error comparison of the actual and ideal time-domain features and the comprehensive consideration of phoneme context information, the corrected phoneme segmentation is more accurate, thus significantly improving the overall accuracy of the speech recognition system.

[0124] Furthermore, the deep speech recognition module includes the following components:

[0125] The context-aware feature adaptation unit performs context-related feature transformation on the normalized features to adapt the model input;

[0126] The multi-language hybrid acoustic unit realizes unified modeling and accurate recognition of cross-language phonemes through the hybrid architecture of a shared phoneme mapping layer and a language-specific classifier;

[0127] The attention-enhanced encoder unit uses the multi-head self-attention mechanism to capture long-distance speech temporal dependencies and strengthen the context correlation modeling of pronunciation-blurred segments;

[0128] The semantic alignment decoder unit dynamically fuses acoustic and language features based on attention weights, performs semantic-level alignment on the basis of basic phoneme alignment, generates text sequences frame by frame and fuses the semantic coherence of the dialogue scene;

[0129] The language model fusion unit integrates the neural language model and improves the recognition accuracy of proper nouns and complex grammar through a shallow fusion strategy;

[0130] The dynamic vocabulary prediction unit dynamically adjusts the output vocabulary probability distribution according to the real-time input language and domain context to adapt to diverse application scenarios;

[0131] The post - processing optimization unit restores punctuation, standardizes the number format, and filters the confidence level of the recognition results, improving the readability of the output and the robustness of the system.

[0132] In summary, the deep speech recognition module realizes high - precision and multi - language speech recognition through multiple units such as context - aware feature adaptation, multi - language hybrid acoustics, attention - enhanced encoding, semantic alignment decoding, language model fusion, dynamic vocabulary prediction, and post - processing optimization. The context - aware feature adaptation unit transforms the features to ensure the accuracy of the input model; the multi - language hybrid acoustics unit realizes the unified modeling of cross - language phonemes through a shared architecture; the attention - enhanced encoder captures long - distance speech dependencies and strengthens context associations; the semantic alignment decoder dynamically fuses features to generate coherent text sequences; the language model fusion unit improves the recognition accuracy of complex grammar; the dynamic vocabulary prediction unit adapts to different languages and application scenarios; and the post - processing optimization unit improves the readability of the output and the robustness of the system. Combining these technical means, the deep speech recognition module significantly improves the overall performance and recognition accuracy of the system.

[0133] Furthermore, based on the dynamic fusion of acoustic and language features by attention weights, semantic - level alignment is performed on the basis of basic phoneme alignment, and text sequences are generated frame by frame and the semantic coherence of the dialogue scenario is fused, including the following steps:

[0134] Perform dynamic weighted fusion on the acoustic feature A t and the language feature L t through the attention mechanism to generate the fusion feature F t at time t. The formula is: F t =α t ·A t +(1 - α t )·L t , where α t is the attention weight at the current moment;

[0135] Calculate the alignment probability between the acoustic feature and the language phoneme to achieve the exact matching of acoustic information and phonemes. The formula is: p(p t |F t ) = exp(score(F t , p t )) / ∑ p′ exp(score(F t , p′)), where p(p t |F t ) represents the conditional probability of generating the phoneme p t at time t given the fusion feature F t ; score(F t , p t ) represents the score based on the fusion feature Ft and the similarity scoring function for the phoneme p t ; p′ represents other phonemes except p t ; score(F t , p′) represents the similarity scoring function based on the fused feature F t and other phonemes p′, which is used to calculate the similarity of different phonemes and normalize it;

[0136] On the basis of phoneme alignment, maximize the conditional probability p(w t ∣F t ) to ensure that the text is semantically consistent with the input audio. The formula is: L semantics =-∑ t＝1 T logp(w t ∣F t ), where L semantics is the loss function for semantic-level alignment, indicating optimizing the semantic consistency between the speech feature F t and the generated text; T represents the number of frames of the input sequence; p(w t ∣F t ) is the conditional probability of generating the text word w t given the fused feature F t ;

[0137] Based on the phoneme and semantic alignment results, combined with the context information C t-1 , generate the text w t frame by frame. The formula is: p(w t ∣F t , C t-1 ) = exp(score(F t , C t-1 , w t )) / ∑ w′ exp(score(F t , C t-1 , w′)), where p(w t ∣F t , C t-1 ) represents the conditional probability of generating the current text word w t given the fused feature F t-1 and the previous context information C t ; score(F t , C t-1 , w t ) represents calculating the similarity score of generating the text word w t at time t given the fused feature F t-1 and the context C t ; score(F t, C t-1 , w′) represents calculating the similarity score for generating the candidate word w′ at time t given the fused feature F t and the context information C t-1 .

[0138] The phoneme alignment, semantic alignment, and dialogue context coherence are optimized through a comprehensive loss function. The formula is: L total = L phoneme + L semantics + L context , where L total为 is the comprehensive loss function, representing the total loss function that simultaneously considers the phoneme alignment loss L phoneme , semantic alignment loss L semantics , and context coherence loss L context .

[0139] In summary, through the attention mechanism, the acoustic features and language features are dynamically fused. On the premise of basic phoneme alignment, semantic-level alignment is performed, and the text sequence is generated frame by frame. At the same time, the semantic coherence of the dialogue scenario is fused. This includes dynamically weighted fusion of acoustic and language features, calculation of the alignment probability between acoustic features and phonemes, optimization of the conditional probability of the generated text, and generation of text frame by frame by combining context information. By integrating these steps, through optimizing phoneme alignment, semantic alignment, and context coherence, the overall performance of speech recognition and semantic understanding ability are significantly improved, ensuring a high degree of semantic consistency between the output text and the input speech.

[0140] Furthermore, the intelligent evaluation engine module includes the following components:

[0141] The language performance scoring unit analyzes the learner's pronunciation, grammar, and intonation, and gives a score based on phoneme and sentence structure features to evaluate the accuracy of their language performance;

[0142] The speech quality analysis unit combines the quality metadata provided by the signal enhancement and echo cancellation unit to analyze the clarity, fluency, and stress features of the learner's speech, and provides a quantitative evaluation of the quality of language expression;

[0143] The grammar and semantic understanding unit performs grammar analysis and semantic understanding based on the learner's language input, identifies grammar errors and semantic deviations in the sentence, and ensures the correctness of the language content;

[0144] The progress and performance comparison unit dynamically evaluates the learner's progress by comparing their current performance with their historical learning data, and identifies the weak points and areas that need to be strengthened in learning;

[0145] The structured feedback generation unit generates standardized JSON format feedback data based on the scoring and analysis results, providing structured input for subsequent visualization;

[0146] Error correction and optimization suggestion unit, which provides specific error correction solutions and optimization suggestions for learners' mistakes, including pronunciation adjustment, grammar modification, and expression optimization, to help them quickly correct and improve their abilities.

[0147] In summary, the intelligent evaluation engine module comprehensively evaluates learners' language performance through components such as language performance scoring, speech quality analysis, grammar and semantic understanding, progress and performance comparison, structured feedback generation, and error correction and optimization suggestions. By analyzing features such as pronunciation, grammar, and intonation, it evaluates the language accuracy; combines signal enhancement and echo cancellation data to analyze speech clarity and fluency; identifies grammar errors and semantic deviations to ensure the correctness of language content; compares historical data to dynamically evaluate progress; generates standardized feedback data for visualization input; and provides specific error correction and optimization suggestions to help learners quickly correct mistakes and improve language abilities. Combining these functions, the intelligent evaluation engine module significantly improves the evaluation accuracy and feedback quality of learners' language performance.

[0148] Furthermore, the personalized learning management module includes the following components:

[0149] Learner portrait construction unit, which constructs a detailed learner portrait by analyzing learners' learning behaviors, preferences, and historical performances, providing basic data for personalized recommendation and learning path planning;

[0150] Deep learning inference engine unit, which uses deep learning algorithms to analyze learners' learning progress and performance, and adjusts the learning path planning in real time to ensure the personalized adaptation of learning content;

[0151] Knowledge graph inference unit, which combines knowledge graph technology to dynamically plan the optimal learning path for learners by reasoning about the relationships and dependencies between knowledge points, ensuring the logical coherence and effectiveness of the content;

[0152] Multi-source inference coordinator unit, which integrates the results of deep learning inference and knowledge graph inference to generate a unified learning path decision;

[0153] Learning progress management unit, which real-time tracks learners' learning progress, automatically adjusts the difficulty of tasks and challenges, and ensures that the learning burden is appropriate and matches the learners' abilities;

[0154] Learning effect prediction unit, which predicts learners' future learning effects based on their historical data and current performances, identifies potential learning bottlenecks, and intervenes in advance;

[0155] The Intervention Suggestion Generation Unit automatically generates personalized intervention suggestions based on the learning effect prediction results and in combination with the feedback from the evaluation engine, including adjusting the learning plan, recommending supplementary materials, and giving review suggestions to help learners overcome difficulties.

[0156] The Learning Plan Adjustment Unit automatically adjusts the personalized learning plan according to the real-time feedback and performance of the learner, optimizes the learning objectives and task arrangements, and improves the learning effect.

[0157] In summary, the personalized learning management module realizes comprehensive personalized learning support for learners through components such as learner portrait construction, deep learning inference, knowledge graph inference, multi-source inference coordination, learning progress management, learning effect prediction, intervention suggestion generation, and learning plan adjustment. By analyzing learning behaviors and preferences, constructing learner portraits, it provides data support for personalized recommendations; uses deep learning and knowledge graph inference to dynamically plan the optimal learning path; tracks the learning progress in real time, adjusts the task difficulty, predicts the learning effect and intervenes; generates personalized intervention suggestions and optimizes the learning plan. Combining these functions, the personalized learning management module effectively improves the adaptability of learning content, the rationality of the learning path, and the predictability of the learning effect, helping learners achieve more efficient personalized learning.

[0158] Furthermore, using deep learning algorithms to analyze the learning progress and performance of learners, and adjusting the learning path planning in real time to ensure the personalized adaptation of learning content, including the following steps:

[0159] Design a deep learning model, input the learner's historical progress, performance, and task characteristics, output personalized learning path adjustment suggestions, and predict the learner's future performance;

[0160] By evaluating the difference between the learner's actual progress and performance and the predetermined goal, to judge whether the learning path needs to be adjusted, which is expressed by the formula: ΔL τ =α·(G τ target -G τ )+β·(P τ target -P τ ), where ΔL τ represents the adjustment amount of the learning path at time step τ; α represents the sensitivity of the learning path adjustment; G τ target represents the target progress of the learner at time step τ; G τ represents the actual progress of the learner at time step τ; β represents the adjustment proportional coefficient in the learning path adjustment process; P τ target represents the target path of the learner at time step τ; P τDenote the current path of the learner at time step τ;

[0161] According to the calculated adjustment amount ΔL τ , dynamically adjust the learning path to ensure that the content matches the learner's current ability. It is expressed by the formula: L τ+1 = L τ + ΔL τ , where L τ+1 denotes the learning path of the learner at time step τ + 1; L τ denotes the current learning path of the learner at time step t;

[0162] Generate personalized feedback and recommended content based on the learning progress and performance evaluation results to help learners overcome bottlenecks and improve learning effects.

[0163] In summary, use deep learning algorithms to analyze the learning progress and performance of learners. By designing the input historical data of the deep learning model, the learning path planning is adjusted in real time, and personalized feedback and recommended content are generated to ensure the personalized adaptation of the learning content. By evaluating the difference between the actual progress of the learner and the goal, the learning path is dynamically adjusted to match the learner's current ability, thereby helping them overcome learning bottlenecks and improve learning effects. This process comprehensively utilizes the prediction ability and real-time adjustment mechanism of the deep learning model, significantly improving the accuracy and effectiveness of personalized learning management.

[0164] Furthermore, the learning interaction experience module includes the following components:

[0165] Visual feedback display unit, which receives and parses the structured feedback data generated by the evaluation engine, and is responsible for displaying the learner's learning progress, scores and feedback to ensure that learners can intuitively see their learning achievements;

[0166] Voice and text interaction unit, which combines speech recognition and natural language processing technologies to provide two-way interaction of voice and text to ensure that learners can interact with the system efficiently through voice or text input;

[0167] Multi-modal feedback fusion unit, which integrates various feedback forms from the evaluation engine to enhance the immersion and interaction experience of learners, making the feedback more intuitive and vivid;

[0168] User interface optimization unit, which continuously optimizes the layout and interaction design of the user interface based on user behavior analysis and feedback to improve the operation efficiency of learners;

[0169] Learning guidance display unit, which displays personalized learning suggestions and guidance generated based on the evaluation results to help learners optimize learning strategies, adjust learning priorities, and improve learning efficiency;

[0170] An interactive guidance and assistance unit that provides real-time guidance and assistance during the learning process, leveraging interactive prompts and virtual assistant functions to help learners solve questions and guide the learning direction.

[0171] In summary, the learning interaction experience module realizes comprehensive interactive support for learners through components such as visual feedback display, voice and text interaction, multi-modal feedback integration, user interface optimization, learning guidance display, and interactive guidance and assistance. This module receives and parses the feedback data generated by the evaluation engine, intuitively displays the learning progress and scores; combines speech recognition and natural language processing technologies to provide efficient two-way interaction; enhances the learner's immersion and interaction experience through the integration of various feedback forms; continuously optimizes the interface layout based on user behavior analysis to improve operation efficiency; displays personalized learning suggestions and guidance to help learners optimize their learning strategies; and provides real-time guidance and assistance during the learning process. Combining these functions, the learning interaction experience module significantly enhances the learner's interaction experience and learning effect.

[0172] Furthermore, the system collaborative control module includes the following components:

[0173] A resource scheduling and management unit that dynamically schedules computing resources according to system load and module requirements, ensuring that each functional module obtains appropriate resource allocation to optimize system performance and response speed;

[0174] An inter-module communication optimization unit that reduces inter-module latency and bandwidth consumption through efficient communication protocols and data transmission optimization algorithms, ensuring high-efficiency interconnection of each module and enhancing the overall system performance;

[0175] A task allocation and priority control unit that intelligently allocates computing tasks and adjusts task priorities according to the urgency and complexity of module tasks, ensuring the timely processing of critical tasks and the stable operation of the system;

[0176] A load balancing and fault tolerance mechanism unit that monitors the load conditions of each module in the system, dynamically allocates tasks through load balancing algorithms, and automatically activates the fault tolerance mechanism in case of failures to ensure the high availability of the system;

[0177] A performance monitoring and feedback unit that real-time monitors the performance data of each functional module and adjusts the scheduling strategy through a feedback mechanism to achieve adaptive performance optimization;

[0178] A global optimization decision-making unit that analyzes the system operation status from a global perspective and automatically generates system performance optimization strategies in combination with machine learning algorithms to ensure that each module works together to reach the optimal state;

[0179] An adaptive adjustment mechanism unit that automatically adjusts the coordination strategy between each module according to system environment and task changes, ensuring that the system continues to operate efficiently and optimizes resource usage under different working conditions.

[0180] In summary, the system collaborative control module realizes the efficient operation and performance optimization of the system through components such as resource scheduling management, inter-module communication optimization, task allocation and priority control, load balancing and fault tolerance mechanisms, performance monitoring and feedback, global optimization decision-making, and adaptive adjustment mechanisms. By dynamically scheduling computing resources and intelligently allocating tasks, it ensures that each functional module obtains appropriate resource allocation; adopts an efficient communication protocol and data transmission optimization algorithm to reduce latency and bandwidth consumption; through load balancing algorithms and fault tolerance mechanisms, it guarantees the high availability of the system; monitors performance data in real time and adjusts the scheduling strategy to achieve adaptive performance optimization; combines machine learning algorithms to generate system performance optimization strategies to ensure that each module works together to reach the optimal state; automatically adjusts the coordination strategy between modules to ensure that the system continues to operate efficiently under different working conditions. Combining these functions, the system collaborative control module significantly improves the overall performance and response speed of the system, ensuring the stability and efficiency of the system.

[0181] Furthermore, monitoring the load situation of each module of the monitoring system, dynamically allocating tasks through load balancing algorithms, and automatically starting the fault tolerance mechanism when a failure occurs, including the following steps:

[0182] Using monitoring tools to collect real-time resource usage data of each module and setting thresholds to trigger alarms;

[0183] Based on the load data, adopting a dynamic load balancing algorithm to adjust task allocation to ensure the reasonable utilization of resources and avoid overload;

[0184] When a certain module is overloaded, the system automatically migrates tasks to a module with a lighter load to ensure that the tasks are completed without interruption;

[0185] When a failure occurs, automatically start redundant modules to take over tasks to ensure that the system operation is not affected;

[0186] Record the fault log and trigger an alarm to ensure that the operation and maintenance personnel can respond and solve problems in a timely manner.

[0187] In summary, by monitoring the load situation of each module of the system in real time and adopting a dynamic load balancing algorithm to adjust task allocation, it ensures the reasonable utilization of resources and avoids overload. When a certain module is overloaded, the system will automatically migrate tasks to a module with a lighter load to ensure that the tasks are not interrupted; when a failure occurs, automatically start redundant modules to take over tasks to ensure that the system operation is not affected. The system will also record the fault log and trigger an alarm to ensure that the operation and maintenance personnel can respond and solve problems in a timely manner. Generally speaking, this mechanism significantly improves the stability and high availability of the system, ensuring the efficient operation of each module under different working conditions.

[0188] Furthermore, analyze the system operation status from a global perspective, and automatically generate system performance optimization strategies in combination with machine learning algorithms to ensure that each module works collaboratively to reach the optimal state, including the following steps:

[0189] Collect system operation data from each module, including resource usage, task processing speed, and response time, to provide a comprehensive data basis for subsequent analysis;

[0190] Based on the collected data, evaluate the overall health of the system, construct a global state model, analyze the resource dependencies and collaborative effects between modules, and identify potential performance bottlenecks and collaborative problems;

[0191] Use machine learning algorithms for feature engineering, extract key features crucial for system performance optimization, and perform data cleaning and preprocessing to ensure data quality;

[0192] According to historical performance data and the global state model, train machine learning algorithms to automatically learn and predict which configurations and scheduling strategies can improve system performance;

[0193] Based on the trained machine learning model, automatically generate the optimal system performance optimization strategy, and optimize the system state for different workloads and operating environments by adjusting the coordination and resource allocation between modules;

[0194] Apply the generated optimization strategy to the system, and monitor its effect in real time. Use the performance feedback mechanism to evaluate the actual effect of the optimization strategy, and continuously adjust and optimize according to the feedback.

[0195] In summary, by analyzing the system operation status from a global perspective and combining machine learning algorithms to automatically generate system performance optimization strategies, it is ensured that each module works collaboratively to reach the optimal state. The specific steps include collecting system operation data, evaluating the overall health, constructing a global state model, using machine learning algorithms for feature engineering, training and predicting optimization strategies, and applying and monitoring the optimization effect in real time. Through these steps, the system can dynamically adjust resource allocation and scheduling strategies, continuously optimize the collaborative effect between modules, and significantly improve the overall performance and operation efficiency of the system.

[0196] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A language learning assistance application system based on speech recognition, characterized in that, It includes the following modules: Intelligent voice acquisition and enhancement module, which realizes the acquisition and preprocessing of high-quality voice signals in complex environments through adaptive noise suppression and multi-channel signal fusion technologies; Multi-modal feature extraction module, which dynamically extracts and fuses multi-dimensional learning feature information based on a hierarchical feature extraction architecture, completes phoneme-level feature analysis and boundary segmentation, and is also responsible for feature normalization and optimization; Deep voice recognition module, which uses a multi-language hybrid acoustic model and attention-enhanced sequence modeling to achieve high-precision voice recognition based on the normalized features; Intelligent evaluation engine module, which constructs a multi-level scoring standard system to realize real-time and multi-dimensional evaluation of learners' language performance, and is responsible for generating evaluation results, personalized feedback content, error correction analysis and optimization suggestions; Personalized learning management module, which integrates evaluation results based on a hybrid inference mechanism of deep learning and knowledge graph to dynamically plan the optimal learning path for learners and manage the learning progress, and also provides learning effect prediction and intervention suggestions; Learning interaction experience module, which is responsible for the visual presentation of learning feedback, multi-modal human-computer interaction design and user interface optimization, and displays the real-time feedback and suggestions generated by the intelligent evaluation engine to ensure the intuitive and effective transmission of learning guidance and error correction suggestions; System coordination and control module, which adopts a distributed resource scheduling strategy and an inter-module communication optimization mechanism to coordinate the efficient operation of each functional module and realize the adaptive optimization of the overall system performance.

2. The language learning assistance application system based on speech recognition according to claim 1, wherein The intelligent voice acquisition and enhancement module includes the following components: Adaptive noise suppression unit, which analyzes the environmental noise in real time and adjusts the noise suppression strategy through an adaptive algorithm to improve the clarity of voice signals; Multi-channel signal fusion unit, which collects voice signals through multiple microphone arrays and fuses data from different channels to improve the spatial resolution and acquisition accuracy of signals; Signal enhancement and echo cancellation unit, which integrates signal enhancement and echo cancellation functions, and optimizes the voice quality and removes interference through gain control, dynamic range compression technology and echo cancellation algorithms; Voice activity detection unit, which accurately identifies voice signals by detecting the start and end of voice activities to avoid the influence of irrelevant noise on subsequent processing; Signal spectrum reconstruction unit, which performs spectrum analysis on voice signals and improves the frequency response range of voices through reconstruction algorithms to ensure high-quality voice output; Data preprocessing and transmission unit, which denoises the enhanced voice signals, removes irrelevant information, optimizes the data transmission format, and ensures the effective transmission of signals to the next-level processing module.

3. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The multi-modal feature extraction module includes the following components: High-order spectrum feature extraction unit, which extracts deep frequency-domain and time-domain features from the preprocessed voice signals to provide audio information for subsequent analysis; Video feature extraction unit, which extracts facial movement and lip movement features in video signals through visual input to help identify language information in multi-modal signals; Voice dynamic feature analysis unit, which analyzes the temporal features of voice signals to provide support for accurate phoneme-level analysis and boundary segmentation; The phoneme analysis and alignment unit combines the time-domain features at the phoneme level and the basic phoneme alignment information to accurately analyze the time-domain features of each phoneme and perform boundary segmentation to ensure the accuracy of speech recognition. The feature fusion and enhancement unit dynamically fuses data from audio, video, and other sensors, and enhances the complementarity of different modality information through a deep learning model to improve the effectiveness of multi-dimensional features. The feature normalization and optimization unit normalizes the extracted features, selects the most representative and discriminative features according to the requirements of the speech recognition task, reduces redundant information, and improves the recognition efficiency and accuracy.

4. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The deep speech recognition module includes the following components: The context-aware feature adaptation unit performs context-related feature transformation on the normalized features to adapt the model input. The multi-language hybrid acoustic unit realizes the unified modeling and accurate recognition of cross-language phonemes through a hybrid architecture of a shared phoneme mapping layer and a language-specific classifier. The attention-enhanced encoder unit uses the multi-head self-attention mechanism to capture the long-distance speech temporal dependencies and strengthens the context-related modeling of the unclear pronunciation segments. The semantic alignment decoder unit dynamically fuses the acoustic and language features based on the attention weights, performs semantic-level alignment on the basis of the basic phoneme alignment, generates the text sequence frame by frame, and integrates the semantic coherence of the dialogue scenario. The language model fusion unit integrates the neural language model and improves the recognition accuracy of proper nouns and complex grammar through a shallow fusion strategy. The dynamic vocabulary prediction unit dynamically adjusts the probability distribution of the output vocabulary according to the real-time input language and domain context to adapt to diverse application scenarios. The post-processing optimization unit performs punctuation restoration, digital format standardization, and confidence filtering on the recognition results to improve the output readability and system robustness.

5. The language learning assistance application system based on speech recognition according to claim 1, wherein The intelligent evaluation engine module includes the following components: The language performance scoring unit gives a score based on the phoneme and sentence structure features by analyzing the learner's pronunciation, grammar, and intonation, and evaluates the accuracy of their language performance. The speech quality analysis unit combines the quality metadata provided by the signal enhancement and echo cancellation unit to analyze the clarity, fluency, and stress features of the learner's speech, and provides a quantitative evaluation of the quality of language expression. The grammar and semantic understanding unit performs grammar analysis and semantic understanding according to the learner's language input, identifies grammar errors and semantic deviations in the sentence, and ensures the correctness of the language content. The progress and performance comparison unit dynamically evaluates the learner's progress by comparing their current performance with their historical learning data, and identifies the weak points and areas that need to be strengthened in learning. The structured feedback generation unit generates standardized JSON format feedback data based on the scoring and analysis results, providing structured input for subsequent visualization. The error correction and optimization suggestion unit provides specific error correction solutions and optimization suggestions for the learner's errors, including pronunciation adjustment, grammar modification, and expression optimization, to help them quickly correct and improve their abilities.

6. The language learning assistance application system based on speech recognition according to claim 1, wherein The personalized learning management module includes the following components: The learner profile construction unit constructs a detailed learner profile by analyzing the learner's learning behaviors, preferences, and historical performance, providing basic data for personalized recommendation and learning path planning; The deep learning inference engine unit uses deep learning algorithms to analyze the learner's learning progress and performance, and adjusts the learning path planning in real time to ensure the personalized adaptation of learning content; The knowledge graph inference unit combines knowledge graph technology to infer the relationships and dependencies between knowledge points, dynamically planning the learner's optimal learning path to ensure the logical coherence and effectiveness of the content; The multi-source inference coordinator unit integrates the results of deep learning inference and knowledge graph inference to generate a unified learning path decision; The learning progress management unit tracks the learner's learning progress in real time, automatically adjusts the difficulty of tasks and challenges, and ensures that the learning burden is appropriate and matches the learner's ability; The learning effect prediction unit predicts the learner's future learning effect based on the learner's historical data and current performance, identifies potential learning bottlenecks, and intervenes in advance; The intervention suggestion generation unit automatically generates personalized intervention suggestions according to the learning effect prediction results, combined with the feedback of the evaluation engine, including adjusting the learning plan, recommending supplementary materials, and giving review suggestions to help the learner overcome difficulties; The learning plan adjustment unit automatically adjusts the personalized learning plan according to the learner's real-time feedback and performance, optimizes the learning goals and task arrangements, and improves the learning effect.

7. The language learning assistance application system based on speech recognition according to claim 1, wherein, The learning interaction experience module includes the following components: The visual feedback display unit receives and parses the structured feedback data generated by the evaluation engine, and is responsible for displaying the learner's learning progress, scores, and feedback, ensuring that the learner can intuitively see their learning achievements; The voice and text interaction unit combines speech recognition and natural language processing technologies to provide two-way interaction between voice and text, ensuring that the learner can interact with the system efficiently through voice or text input; The multi-modal feedback fusion unit integrates various feedback forms from the evaluation engine to enhance the learner's immersion and interaction experience, making the feedback more intuitive and vivid; The user interface optimization unit continuously optimizes the layout and interaction design of the user interface based on user behavior analysis and feedback, improving the learner's operation efficiency; The learning guidance display unit displays personalized learning suggestions and guidance generated based on the evaluation results to help the learner optimize learning strategies, adjust learning priorities, and improve learning efficiency; The interactive guidance and help unit provides real-time guidance and help during the learning process, using interactive prompts and virtual assistant functions to help the learner solve questions and guide the learning direction.

8. The language learning assistance application system based on speech recognition according to claim 1, characterized in that The system coordination control module includes the following components: The resource scheduling and management unit dynamically schedules computing resources according to the system load and module requirements, ensuring that each functional module obtains appropriate resource allocation to optimize the system performance and response speed; The inter-module communication optimization unit reduces the delay and bandwidth consumption between modules through efficient communication protocols and data transmission optimization algorithms, ensuring the efficient interconnection of each module and improving the overall system performance; The task allocation and priority control unit intelligently allocates computing tasks and adjusts task priorities according to the urgency and complexity of module tasks, ensuring the timely processing of critical tasks and the stable operation of the system; The load balancing and fault tolerance mechanism unit monitors the load conditions of each module in the system, dynamically allocates tasks through load balancing algorithms, and automatically activates the fault tolerance mechanism in case of failures, ensuring the high availability of the system; The performance monitoring and feedback unit monitors the performance data of each functional module in real time and adjusts the scheduling strategy through a feedback mechanism to achieve adaptive performance optimization; The global optimization decision-making unit analyzes the system operation status from a global perspective, and automatically generates system performance optimization strategies in combination with machine learning algorithms to ensure that each module works together to reach the optimal state; The adaptive adjustment mechanism unit automatically adjusts the coordination strategy between modules according to system environment and task changes, ensuring that the system continues to operate efficiently and optimizes resource utilization under different working conditions.

9. The language learning assistance application system based on speech recognition according to claim 8, wherein Monitor the load conditions of each module in the system, dynamically allocate tasks through load balancing algorithms, and automatically activate the fault tolerance mechanism in case of failures, including the following steps: Use monitoring tools to collect the resource usage data of each module in real time and set thresholds to trigger alarms; Based on the load data, adopt dynamic load balancing algorithms to adjust task allocation, ensure the reasonable utilization of resources, and avoid overload; When a module is overloaded, the system automatically migrates tasks to modules with lighter loads to ensure the uninterrupted completion of tasks; When a failure occurs, automatically activate redundant modules to take over tasks to ensure that the system operation is not affected; Record the fault logs and trigger alarms to ensure that operation and maintenance personnel can respond and solve problems in a timely manner.

10. The language learning assistance application system based on speech recognition according to claim 8, wherein, Analyze the system operation status from a global perspective, and automatically generate system performance optimization strategies in combination with machine learning algorithms to ensure that each module works together to reach the optimal state, including the following steps: Collect system operation data from each module, including resource usage, task processing speed, and response time, providing a comprehensive data basis for subsequent analysis; Based on the collected data, evaluate the overall health of the system, build a global state model, analyze the resource dependencies and synergy effects between modules, and identify potential performance bottlenecks and coordination problems; Use machine learning algorithms for feature engineering, extract key features crucial for system performance optimization, and perform data cleaning and preprocessing to ensure data quality; According to historical performance data and the global state model, train machine learning algorithms to automatically learn and predict which configurations and scheduling strategies can improve system performance; Based on the trained machine learning model, automatically generate the optimal system performance optimization strategy, optimize the system state for different workloads and operating environments by adjusting the coordination and resource allocation between modules; Apply the generated optimization strategy to the system and monitor its effects in real time, use the performance feedback mechanism to evaluate the actual effects of the optimization strategy, and continuously adjust and optimize according to the feedback.

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