Language learning assistance system based on speech recognition

Abstract

This application discloses a language learning assistance application system based on speech recognition, belonging to the field of learning assistance technology. The system includes: an intelligent speech acquisition and enhancement module, which acquires and preprocesses high-quality speech signals in complex environments; a multimodal feature extraction module, which dynamically extracts and fuses multi-dimensional learning feature information, performs phoneme-level feature analysis and boundary segmentation, and is responsible for feature normalization and optimization; a deep speech recognition module, which achieves high-precision speech recognition based on normalized features; an intelligent evaluation engine module, which enables real-time, multi-dimensional evaluation of learners' language performance; a personalized learning management module, which integrates evaluation results to dynamically plan the optimal learning path for learners and manage their learning progress, while also providing learning effect prediction and intervention suggestions; and a learning interaction experience module, responsible for the visual presentation of learning feedback, multimodal human-computer interaction design, and user interface optimization.

Description

Technical Field

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

[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 systems based on speech recognition are gradually becoming a trend. These systems, by combining speech recognition technology with language learning, can effectively improve learners' oral expression skills and help them enhance their practical language application abilities.

[0003] Currently, there are 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 noisy environments, where it cannot effectively identify non-standard pronunciations, thus limiting the learning effect. Second, existing application systems are mostly single-function, such as voice input or spoken language correction, lacking a systematic language learning path and multi-dimensional learning feedback. In addition, most existing systems ignore the learner's personalized needs and cannot automatically adjust the learning content and difficulty according to the learner's learning progress and level, resulting in low learning efficiency.

[0004] In conclusion, how to improve the accuracy of speech recognition, combine it with a multi-dimensional learning feedback mechanism, provide personalized learning paths, and ensure the stability and efficiency of the system in different environments has become an urgent technical problem to be solved. Summary of the Invention

[0005] In order to overcome a series of shortcomings in the existing technology, the purpose of this application is to provide a language learning assistance application system based on speech recognition, which includes the following modules:

[0006] The intelligent voice acquisition and enhancement module achieves high-quality voice signal acquisition and preprocessing in complex environments through adaptive noise suppression and multi-channel signal fusion technology;

[0007] The multimodal feature extraction module, based on a hierarchical feature extraction architecture, dynamically extracts and fuses multi-dimensional learned feature information, and completes phoneme-level feature analysis and boundary segmentation, while also being responsible for feature normalization and optimization.

[0008] The deep speech recognition module utilizes a multilingual hybrid acoustic model and attention-enhanced sequence modeling to achieve high-precision speech recognition based on normalized features;

[0009] The intelligent assessment engine module constructs a multi-level scoring standard system to achieve real-time, multi-dimensional assessment of learners' language performance, and is responsible for generating assessment 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 graphs, integrates evaluation results to dynamically plan the optimal learning path for learners and manage their learning progress, while also providing learning effect prediction and intervention suggestions.

[0011] The learning interaction experience module is responsible for the visualization of learning feedback, multimodal human-computer interaction design, and user interface optimization. It displays real-time feedback and suggestions generated by the intelligent evaluation engine to ensure the intuitive and effective delivery 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 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 environmental noise in real time and adjusts the noise suppression strategy through an adaptive algorithm to improve the clarity of the speech signal.

[0015] The multi-channel signal fusion unit collects speech 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. It optimizes speech quality and removes interference through gain control, dynamic range compression technology and echo cancellation algorithm.

[0017] The voice activity detection unit accurately identifies voice signals by detecting the start and end of voice activities, thus avoiding irrelevant noise from affecting subsequent processing.

[0018] The signal spectrum reconstruction unit performs spectrum analysis on the speech signal and improves the frequency response range of the speech through reconstruction algorithms to ensure high-quality speech output.

[0019] The data preprocessing and transmission unit reduces noise, removes irrelevant information, and optimizes the data transmission format of the enhanced speech signal to ensure that the signal is effectively transmitted to the next processing module.

[0020] Furthermore, the multimodal feature extraction module includes the following components:

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

[0022] The video feature extraction unit extracts facial movement and lip features from video signals through visual input, helping to identify language information in multimodal signals;

[0023] The speech dynamic feature analysis unit analyzes the temporal features of speech signals, providing support for accurate phoneme-level analysis and boundary segmentation;

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

[0025] The feature fusion and enhancement unit dynamically fuses data from audio, video, and other sensors, and enhances the complementarity of information from different modalities through a deep learning model, thereby improving the effectiveness of multi-dimensional features.

[0026] The feature normalization and optimization unit standardizes the extracted features and selects the most representative and discriminative features according to the needs of the speech recognition task, reducing redundant information and improving recognition efficiency and accuracy.

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

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

[0029] The multilingual hybrid acoustic unit achieves 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] The attention-enhanced encoder unit employs a multi-head self-attention mechanism to capture long-distance speech temporal dependencies and strengthens the contextual association modeling of ambiguous speech segments;

[0031] The semantic alignment decoder unit dynamically fuses acoustic and linguistic features based on attention weights, performs semantic-level alignment on basic phoneme alignment, generates text sequences frame by frame, and integrates the semantic coherence of the dialogue scene.

[0032] The 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] The dynamic vocabulary prediction unit dynamically adjusts the probability distribution of the output vocabulary based on the real-time input language and domain context to adapt to diverse application scenarios.

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

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

[0036] The language performance assessment unit analyzes learners' pronunciation, grammar, and intonation, and provides a score based on phoneme and sentence structure features to assess the accuracy of their language performance.

[0037] The speech quality analysis unit, combined with the quality metadata provided by the signal enhancement and echo cancellation unit, analyzes the clarity, fluency, and stress characteristics of learners' speech, providing a quantitative assessment of the quality of language expression.

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

[0039] The progress and performance comparison unit dynamically assesses learners' progress by comparing their current performance with their historical learning data, and identifies weaknesses and areas that need improvement in their learning.

[0040] The structured feedback generation unit generates standardized JSON-formatted feedback data based on 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 learners' mistakes, 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 building unit constructs detailed learner profiles by analyzing learners' learning behaviors, preferences, and historical performance, providing basic data for personalized recommendations and learning path planning;

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

[0045] The knowledge graph reasoning unit combines knowledge graph technology to reason about the relationships and dependencies between knowledge points, dynamically planning the learner's optimal learning path and ensuring the logical coherence and effectiveness of the content.

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

[0047] The learning progress management unit tracks learners' progress in real time and automatically adjusts the difficulty of tasks and challenges to ensure that the learning burden is appropriate and matches the learner's ability.

[0048] The learning outcome prediction unit predicts future learning outcomes based on learners' historical data and current performance, identifies potential learning bottlenecks, and intervenes in advance.

[0049] The intervention suggestion generation unit automatically generates personalized intervention suggestions based on the learning outcome prediction results and feedback from the assessment engine. These suggestions include adjusting the learning plan, recommending supplementary materials, and providing review suggestions to help learners overcome difficulties.

[0050] The learning plan adjustment unit automatically adjusts personalized learning plans based on learners' real-time feedback and performance, optimizing learning goals and task arrangements to improve learning outcomes.

[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 assessment engine, and is responsible for displaying learners' learning progress, scores and feedback, ensuring that learners can intuitively see their learning outcomes;

[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 efficiently with the system through voice or text input.

[0054] The multimodal feedback fusion unit integrates various feedback formats from the assessment engine to enhance learners' immersion and interactive experience, making 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, thereby improving learners' operational efficiency.

[0056] The learning guidance showcases personalized learning suggestions and guidance generated based on assessment results, helping learners optimize their learning strategies, adjust their learning priorities, and improve their learning efficiency.

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

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

[0059] The resource scheduling and management unit dynamically schedules computing resources based on system load and module requirements to ensure that each functional module receives appropriate resource allocation in order to optimize system performance and response speed.

[0060] The inter-module communication optimization unit reduces latency and bandwidth consumption between modules through efficient communication protocols and data transmission optimization algorithms, ensuring efficient interconnection of modules and improving overall system performance.

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

[0062] The load balancing and fault tolerance mechanism unit monitors the load status of each module in the system, dynamically allocates tasks through the load balancing algorithm, and automatically activates the fault tolerance mechanism when a fault occurs to ensure the high availability of the system.

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

[0064] The global optimization decision-making unit analyzes the system's operating status from a global perspective and automatically generates system performance optimization strategies by combining machine learning algorithms to ensure that all modules work together to achieve the optimal state.

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

[0066] Furthermore, the system monitors the load of each module, dynamically allocates tasks using a load balancing algorithm, and automatically activates a fault-tolerance mechanism in case of a failure, including the following steps:

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

[0068] Based on load data, a dynamic load balancing algorithm is used to adjust task allocation, ensuring the rational use of resources and avoiding overload;

[0069] When a module becomes overloaded, the system automatically migrates the task to a less loaded module to ensure that the task is completed without interruption.

[0070] In the event of a fault, a redundant module is automatically activated to take over the task, ensuring that system operation is not affected.

[0071] Record fault logs and trigger alarms to ensure that maintenance personnel can respond and resolve issues in a timely manner.

[0072] Furthermore, based on a global perspective, the system's operational status is analyzed, and machine learning algorithms are used to automatically generate system performance optimization strategies to ensure that all modules work together to achieve optimal performance. This includes the following steps:

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

[0074] Based on the collected data, assess the overall health of the system, build a global state model, analyze the resource dependencies and synergies between modules, and identify potential performance bottlenecks and synergy issues.

[0075] We use machine learning algorithms for feature engineering to extract key features that are crucial to system performance optimization, and perform data cleaning and preprocessing to ensure data quality.

[0076] Based on historical performance data and global state models, machine learning algorithms are trained to automatically learn and predict which configuration and scheduling strategies can improve system performance.

[0077] Based on the trained machine learning model, the optimal system performance optimization strategy is automatically generated. By adjusting the coordination and resource allocation between modules, the system state is optimized for different workloads and operating environments.

[0078] The generated optimization strategy is applied to the system, and its effect is monitored in real time. The actual effect of the optimization strategy is evaluated using a performance feedback mechanism, and adjustments and optimizations are made continuously based on the feedback.

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

[0080] This application integrates modules such as intelligent voice acquisition, deep speech recognition, personalized learning management, and system collaborative control to achieve efficient voice learning evaluation and dynamic personalized learning path optimization in complex environments. Attached Figure Description

[0081] Figure 1 This is a schematic diagram of the structure of a speech recognition-based language learning assistance application system disclosed in an embodiment of this application. Detailed Implementation

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

[0083] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

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

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

[0086] The intelligent voice acquisition and enhancement module achieves high-quality voice signal acquisition and preprocessing in complex environments through adaptive noise suppression and multi-channel signal fusion technology;

[0087] The multimodal feature extraction module, based on a hierarchical feature extraction architecture, dynamically extracts and fuses multi-dimensional learned feature information, and completes phoneme-level feature analysis and boundary segmentation, while also being responsible for feature normalization and optimization.

[0088] The deep speech recognition module utilizes a multilingual hybrid acoustic model and attention-enhanced sequence modeling to achieve high-precision speech recognition based on normalized features;

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

[0090] The personalized learning management module, based on a hybrid reasoning mechanism of deep learning and knowledge graphs, integrates evaluation results to dynamically plan the optimal learning path for learners and manage their learning progress, while also providing learning effect prediction and intervention suggestions.

[0091] The learning interaction experience module is responsible for the visualization of learning feedback, multimodal human-computer interaction design, and user interface optimization. It displays real-time feedback and suggestions generated by the intelligent evaluation engine to ensure the intuitive and effective delivery of learning guidance and error correction suggestions.

[0092] 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 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 acquiring and preprocessing high-quality voice signals in complex environments. Through adaptive noise suppression technology, this module can dynamically adjust noise reduction parameters to cope with various background noises, such as street noise, indoor reverberation, and wind noise, ensuring the clarity and integrity of the voice signal. Simultaneously, multi-channel signal fusion technology utilizes multiple microphone arrays to enhance the target voice signal through spatial information processing, reducing 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. Furthermore, the intelligent voice acquisition and enhancement module also possesses self-learning capabilities, continuously optimizing parameters based on actual usage to improve adaptability and robustness, ensuring high-quality voice acquisition results in various environments.

[0094] In this embodiment, the multimodal feature extraction module is a crucial component of the system, designed to extract and fuse multi-dimensional learning feature information. Based on a hierarchical feature extraction architecture, this module can dynamically analyze various levels of speech signals, including phoneme-level features and high-level semantic information. This hierarchical feature extraction method enables the system to more accurately capture subtle differences in language while effectively fusing features from different levels. To ensure feature standardization and optimization, the module also introduces advanced feature normalization techniques. By normalizing feature data, it eliminates differences between features, improving their expressive power and consistency. This process not only helps improve the accuracy of speech recognition but also provides a reliable foundation for the multi-dimensional evaluation of the intelligent evaluation engine.

[0095] In this embodiment, the deep speech recognition module utilizes a multilingual hybrid acoustic model and attention-enhanced sequence modeling technology to achieve high-precision speech recognition. The multilingual hybrid acoustic model enhances the model's generalization ability by fusing acoustic features from multiple languages, maintaining high recognition accuracy when processing speech inputs in different languages. Meanwhile, the attention-enhanced sequence modeling technology, by introducing an attention mechanism, enables the model to better capture important information in the speech signal, improving recognition accuracy and robustness. The normalized features are fully utilized in the deep speech recognition module, allowing the system to maintain high recognition rates and processing speeds under various complex speech input conditions. Furthermore, this module possesses adaptive learning capabilities, continuously optimizing the model based on user speech data to improve personalized recognition performance.

[0096] In this embodiment, the intelligent assessment engine module constructs a multi-level scoring standard system to achieve real-time, multi-dimensional assessment of learners' language performance. This module not only performs basic accuracy and fluency analysis of speech signals but also comprehensively assesses multiple dimensions such as learners' speech expression, intonation, and rhythm, thereby generating comprehensive assessment results. Based on these assessment results, the intelligent assessment engine can provide personalized feedback and error correction analysis to help learners identify and improve shortcomings in their language learning. Furthermore, this module also has an optimization suggestion generation function, which can provide targeted optimization suggestions based on the learner's specific situation, helping them improve their language skills more efficiently. The real-time assessment and feedback mechanism allows learners to understand their progress and shortcomings in a timely manner during the learning process, contributing to continuous improvement and enhancement of language learning outcomes.

[0097] In this embodiment, the personalized learning management module integrates the evaluation results of the intelligent assessment engine through a hybrid reasoning mechanism combining deep learning and knowledge graphs. This allows for the dynamic planning of optimal learning paths and the management of learning progress for learners. Based on individual differences and learning needs, the module can adjust learning content and plans in real time, ensuring both efficiency and personalization in the learning process. Furthermore, the personalized learning management module also features learning outcome prediction and intervention suggestions. It can predict future learning outcomes based on learners' progress and performance, and provide targeted intervention suggestions to help learners overcome learning bottlenecks and improve learning efficiency. Through refined learning path planning and progress management, this module provides learners with personalized and dynamic learning guidance, ensuring continuous progress and optimization in their language learning process.

[0098] In this embodiment, the learning interaction experience module provides learners with intuitive and effective learning feedback and suggestions through multimodal human-computer interaction design and user interface optimization. Real-time feedback and optimization suggestions generated by the intelligent assessment engine are presented visually through the learning interaction experience module, enabling learners to intuitively understand their learning progress and areas for improvement. Simultaneously, this module emphasizes the design of the human-computer interaction experience, enhancing the interactivity and engagement of the learning process through multimodal interaction methods such as voice, touch, and gestures. The optimized user interface design not only improves the system's usability and aesthetics but also enhances the learner's user experience and learning motivation. Through the learning interaction experience module, learners can more easily obtain learning feedback and suggestions, helping to improve their learning outcomes 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 dynamically adjusts the resource allocation of each module according to the actual system load, ensuring that the system maintains high performance and response speed even under high concurrency and complex task processing. Simultaneously, the inter-module communication optimization mechanism reduces communication latency and data loss between modules by optimizing data transmission and processing flows, improving the overall collaborative efficiency and reliability of the system. The system collaborative control module also has an adaptive optimization function, continuously optimizing the parameters and strategies of each module based on system operating conditions and user feedback, improving the system's stability and robustness. Through this module, the system can efficiently coordinate various functional modules in complex language learning tasks, ensuring overall system performance and user experience.

[0100] In summary, this speech recognition-based language learning assistance system achieves 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, multimodal feature extraction, deep speech recognition, intelligent evaluation engine, personalized learning management, learning interaction experience, and system collaborative control. Each module has its own unique technical features, working together to build an efficient, intelligent, and personalized language learning assistance system, providing learners with comprehensive and dynamic learning support and guidance. This system not only improves the efficiency and effectiveness of language learning but also provides important technical references and application demonstrations for the development of intelligent education. Through continuous optimization and improvement, the technical effects of each module will be further enhanced, bringing learners a higher quality learning experience and better results.

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

[0102] The adaptive noise suppression unit analyzes environmental noise in real time and adjusts the noise suppression strategy through an adaptive algorithm to improve the clarity of the speech signal.

[0103] The multi-channel signal fusion unit 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] The signal enhancement and echo cancellation unit integrates signal enhancement and echo cancellation functions. It optimizes speech quality and removes interference through gain control, dynamic range compression technology and echo cancellation algorithm.

[0105] The voice activity detection unit accurately identifies voice signals by detecting the start and end of voice activities, thus avoiding irrelevant noise from affecting subsequent processing.

[0106] The signal spectrum reconstruction unit performs spectrum analysis on the speech signal and improves the frequency response range of the speech through reconstruction algorithms to ensure high-quality speech output.

[0107] The data preprocessing and transmission unit reduces noise, removes irrelevant information, and optimizes the data transmission format of the enhanced speech signal to ensure that the signal is effectively transmitted to the next processing module.

[0108] In this embodiment, the intelligent voice acquisition and enhancement module achieves high-quality voice acquisition and processing in complex environments 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 voice signal by analyzing 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 voice quality and removes interference through various techniques; the voice activity detection unit avoids the influence of irrelevant noise by accurately identifying voice activity; the signal spectrum reconstruction unit improves the frequency response range of the voice through spectrum analysis and reconstruction algorithms, ensuring high-quality voice output; finally, the data preprocessing and transmission unit optimizes the data transmission format by reducing noise and removing irrelevant information, ensuring effective signal transmission. The combined effect of these units enables the intelligent voice acquisition and enhancement module to maintain high-quality voice acquisition and processing results in complex and changing environments.

[0109] Furthermore, the multimodal feature extraction module includes the following components:

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

[0111] The video feature extraction unit extracts facial movement and lip features from video signals through visual input, helping to identify language information in multimodal signals;

[0112] The speech dynamic feature analysis unit analyzes the temporal features of speech signals, providing support for accurate phoneme-level analysis and boundary segmentation;

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

[0114] The feature fusion and enhancement unit dynamically fuses data from audio, video, and other sensors, and enhances the complementarity of information from different modalities through a deep learning model, thereby improving the effectiveness of multi-dimensional features.

[0115] The feature normalization and optimization unit standardizes the extracted features and selects the most representative and discriminative features according to the needs of the speech recognition task, reducing redundant information and improving recognition efficiency and accuracy.

[0116] In this embodiment, the multimodal feature extraction module comprehensively extracts and fuses multi-dimensional learning feature information through its six units: high-order spectral 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 spectral feature extraction unit extracts deep features from the speech signal; the video feature extraction unit enhances the language information parsing of multimodal signals through facial motion and lip-shape feature recognition; and the speech dynamic feature analysis unit supports precise phoneme-level analysis and boundary segmentation through temporal feature analysis. The phoneme analysis and alignment unit combines temporal features and basic phoneme alignment information to ensure high accuracy in speech recognition. The feature fusion and enhancement unit enhances the complementarity of different modal information and improves feature effectiveness through deep learning models. The feature normalization and optimization unit selects the most representative and discriminative features through standardization processing, reducing redundant information and thus improving recognition efficiency and accuracy. By combining these techniques, the multimodal feature extraction module significantly improves the overall performance and accuracy of the speech recognition system.

[0117] Furthermore, by combining phoneme-level temporal features and basic phoneme alignment information, the temporal features of each phoneme are precisely analyzed and boundary segmentation is performed, including the following steps:

[0118] The start time of each phoneme is determined using a phoneme-based alignment tool. i and End Time i ;

[0119] Based on the acquired phoneme alignment information, the temporal features x of each phoneme are extracted from the original speech signal x(t). i (t);

[0120] By minimizing the temporal features and the ideal temporal features x ideal The error between (t) yields the preliminary boundary of the phoneme, expressed by the formula: Among them, Boundary i Indicates the boundary position of the i-th phoneme;

[0121] By combining temporal features and energy assessment, the boundary position is optimized, and the boundary error and feature energy difference are minimized to obtain accurate phoneme boundaries, expressed by the formula: Cost iλ represents the boundary segmentation cost function for phoneme i; λ is the regularization coefficient, used to balance the accuracy and computational complexity of phoneme segmentation, and to prevent 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 contextual information of each phoneme, the phoneme segmentation after boundary correction is made more accurate, thereby improving the overall accuracy of speech recognition.

[0123] In this embodiment, by combining the analysis of phoneme-level temporal features and basic phoneme alignment information, the start and end times of each phoneme can be accurately determined. Temporal features are extracted from the original speech signal, and the error between the temporal features and the ideal features is minimized to obtain the initial boundary positions. The boundary positions are further optimized by combining temporal features and energy assessment to ensure high accuracy of phoneme boundary segmentation. Regularization coefficients are used to balance segmentation accuracy and computational complexity. Finally, by comparing the errors of actual and ideal temporal features and comprehensively considering phoneme context information, the corrected phoneme segmentation is more accurate, thereby 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-dependent feature transformation on the normalized features to adapt them to the model input;

[0126] The multilingual hybrid acoustic unit achieves unified modeling and accurate recognition of cross-language phonemes through a hybrid architecture of a shared phoneme mapping layer and a language-specific classifier.

[0127] The attention-enhanced encoder unit employs a multi-head self-attention mechanism to capture long-distance speech temporal dependencies and strengthens the contextual association modeling of ambiguous speech segments;

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

[0129] The 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.

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

[0131] The post-processing optimization unit performs punctuation restoration, digital format standardization, and confidence filtering on the recognition results to improve output readability and system robustness.

[0132] In summary, the deep speech recognition module achieves high-precision, multilingual speech recognition through multiple units, including context-aware feature adaptation, multilingual 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 features to ensure the accuracy of the input model; the multilingual hybrid acoustics unit achieves unified modeling of cross-language phonemes through a shared architecture; the attention-enhanced encoder captures long-distance speech dependencies, strengthening contextual association; the semantic alignment decoder dynamically fuses features to generate coherent text sequences; the language model fusion unit improves the accuracy of complex grammar recognition; 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. By combining these technologies, the deep speech recognition module significantly improves the overall performance and recognition accuracy of the system.

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

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

[0135] The alignment probability between acoustic features and language phonemes is calculated to achieve precise 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 a given fusion feature F t At time t, phoneme p is generated. t conditional probability; score(F) t ,p t ) represents based on fusion feature Ft and phoneme p t The similarity scoring function; p′ represents the similarity score except for p. t Other phonemes besides; score(F) t ,p′) represents based on fusion feature F t A similarity scoring function with other phonemes p′ is used to calculate the similarity between different phonemes and normalize them;

[0136] Based on phoneme alignment, maximize the conditional probability p(w) of the generated text. t |F t To ensure 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 Let F be the loss function for semantic alignment, representing the optimization of speech features. t Semantic consistency between the input and generated text; T represents the number of frames in the input sequence; p(w t |F t Given the fusion feature F t Generate text vocabulary w t The conditional probability;

[0137] Based on phoneme and semantic alignment results, combined with contextual information C t-1 Generate text frame by frame w t 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 a given fusion feature F t And the previous context information C t-1 Generate the current text vocabulary w t conditional probability; score(F) t C t-1 ,w t ) represents the fusion feature F given at time t. t and context C t-1 Calculate and generate text vocabulary w t Similarity score; score(F) tC t-1 ,w′) represents the fusion feature F given at time t. t and context information C t-1 Calculate the similarity score of the generated candidate words w′;

[0138] The phoneme alignment, semantic alignment, and dialogue contextual coherence are optimized by integrating a loss function, as shown in the formula: L total =L phoneme +L semantics +L context , where L total为 The comprehensive loss function represents the loss L that is considered simultaneously with phoneme alignment. phoneme Semantic alignment loss L semantics and context coherence loss L context The total loss function.

[0139] In summary, by dynamically fusing acoustic and linguistic features through an attention mechanism, semantic alignment is performed based on basic phoneme alignment, and text sequences are generated frame by frame, while also incorporating the semantic coherence of the dialogue scenario. This includes dynamically weighted fusion of acoustic and linguistic features, calculation of the alignment probability between acoustic features and phonemes, optimization of the conditional probability of generated text, and frame-by-frame text generation incorporating contextual information. By combining these steps and optimizing phoneme alignment, semantic alignment, and contextual coherence, the overall performance and semantic understanding capabilities of speech recognition 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 assessment unit analyzes learners' pronunciation, grammar, and intonation, and provides a score based on phoneme and sentence structure features to assess the accuracy of their language performance.

[0142] The speech quality analysis unit, combined with the quality metadata provided by the signal enhancement and echo cancellation unit, analyzes the clarity, fluency, and stress characteristics of learners' speech, providing a quantitative assessment of the quality of language expression.

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

[0144] The progress and performance comparison unit dynamically assesses learners' progress by comparing their current performance with their historical learning data, and identifies weaknesses and areas that need improvement in their learning.

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

[0146] The error correction and optimization suggestion unit 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 assessment engine module comprehensively evaluates learners' language performance through components such as language performance scoring, speech quality analysis, grammatical and semantic understanding, progress and performance comparison, structured feedback generation, and error correction and optimization suggestions. It assesses language accuracy by analyzing features such as pronunciation, grammar, and intonation; analyzes speech clarity and fluency by combining signal enhancement and echo cancellation data; identifies grammatical errors and semantic deviations to ensure correct language content; dynamically assesses progress by comparing historical data; generates standardized feedback data to provide input for visualization; and provides specific error correction and optimization suggestions to help learners quickly correct errors and improve their language skills. By combining these functions, the intelligent assessment engine module significantly improves the accuracy and quality of learners' language performance assessments and feedback.

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

[0149] The learner profile building unit constructs detailed learner profiles by analyzing learners' learning behaviors, preferences, and historical performance, providing basic data for personalized recommendations and learning path planning;

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

[0151] The knowledge graph reasoning unit combines knowledge graph technology to reason about the relationships and dependencies between knowledge points, dynamically planning the learner's optimal learning path and ensuring the logical coherence and effectiveness of the content.

[0152] The multi-source reasoning coordinator unit integrates the results of deep learning reasoning and knowledge graph reasoning to generate a unified learning path decision.

[0153] The learning progress management unit tracks learners' progress in real time and automatically adjusts the difficulty of tasks and challenges to ensure that the learning burden is appropriate and matches the learner's ability.

[0154] The learning outcome prediction unit predicts future learning outcomes based on learners' historical data and current performance, identifies potential learning bottlenecks, and intervenes in advance.

[0155] The intervention suggestion generation unit automatically generates personalized intervention suggestions based on the learning outcome prediction results and feedback from the assessment engine. These suggestions include adjusting the learning plan, recommending supplementary materials, and providing review suggestions to help learners overcome difficulties.

[0156] The learning plan adjustment unit automatically adjusts personalized learning plans based on learners' real-time feedback and performance, optimizing learning goals and task arrangements to improve learning outcomes.

[0157] In summary, the personalized learning management module provides comprehensive personalized learning support for learners through components such as learner profiling, deep learning inference, knowledge graph inference, multi-source inference coordination, learning progress management, learning outcome prediction, intervention suggestion generation, and learning plan adjustment. By analyzing learning behaviors and preferences, it constructs learner profiles to provide data support for personalized recommendations; it dynamically plans optimal learning paths using deep learning and knowledge graph inference; it tracks learning progress in real time, adjusts task difficulty, predicts learning outcomes, and intervenes accordingly; and it generates personalized intervention suggestions to optimize learning plans. By combining these functions, the personalized learning management module effectively improves the adaptability of learning content, the rationality of learning paths, and the predictability of learning outcomes, helping learners achieve more efficient personalized learning.

[0158] Furthermore, deep learning algorithms are used to analyze learners' learning progress and performance, and learning path planning is adjusted in real time to ensure personalized adaptation of learning content. This includes the following steps:

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

[0160] By assessing the difference between learners' actual progress and performance and the predetermined goals, it is determined whether the learning path needs to be adjusted, which can be expressed by the formula: ΔL τ =α·(G τ target -G τ )+β·(P τ target -P τ ), where ΔL τ G represents the adjustment amount of the learning path at time step τ; α represents the sensitivity of the learning path adjustment; τ target G represents the learner's target progress at time step τ; τ β represents the learner's actual progress at time step τ; β represents the adjustment ratio coefficient during the learning path adjustment process; P τ target P represents the learner's target path at time step τ; τThis represents the learner's current path at time step τ;

[0161] Based on the calculated adjustment amount ΔL τ The learning path is dynamically adjusted to ensure that the content matches the learner's current ability, which can be expressed by the formula: L τ+1 =L τ +ΔL τ Among them, L τ+1 L represents the learner's learning path at time step τ+1; τ This represents the learner's current learning path at time step t;

[0162] Based on learning progress and performance evaluation results, personalized feedback and recommendations are generated to help learners overcome bottlenecks and improve learning outcomes.

[0163] In summary, by analyzing learners' learning progress and performance using deep learning algorithms, and by designing deep learning models that input historical data, the learning path planning is adjusted in real time, generating personalized feedback and recommended content to ensure personalized adaptation of learning content. By assessing the difference between learners' actual progress and goals, the learning path is dynamically adjusted to match the learner's current abilities, thereby helping them overcome learning bottlenecks and improve learning outcomes. This process comprehensively utilizes the predictive power and real-time adjustment mechanism of deep learning models, significantly improving the accuracy and effectiveness of personalized learning management.

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

[0165] The visual feedback display unit receives and parses the structured feedback data generated by the assessment engine, and is responsible for displaying learners' learning progress, scores and feedback, ensuring that learners can intuitively see their learning outcomes;

[0166] 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 efficiently with the system through voice or text input.

[0167] The multimodal feedback fusion unit integrates various feedback formats from the assessment engine to enhance learners' immersion and interactive experience, making feedback more intuitive and vivid.

[0168] The User Interface Optimization Unit continuously optimizes the layout and interaction design of the user interface based on user behavior analysis and feedback, thereby improving learners' operational efficiency.

[0169] The learning guidance showcases personalized learning suggestions and guidance generated based on assessment results, helping learners optimize their learning strategies, adjust their learning priorities, and improve their learning efficiency.

[0170] The interactive guidance and assistance unit provides real-time guidance and help during the learning process, using interactive prompts and virtual assistant functions to help learners resolve questions and guide their learning direction.

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

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

[0173] The resource scheduling and management unit dynamically schedules computing resources based on system load and module requirements to ensure that each functional module receives appropriate resource allocation in order to optimize system performance and response speed.

[0174] The inter-module communication optimization unit reduces latency and bandwidth consumption between modules through efficient communication protocols and data transmission optimization algorithms, ensuring efficient interconnection of modules and improving overall system performance.

[0175] The task allocation and priority control unit intelligently allocates computing tasks and adjusts task priorities based on the urgency and complexity of module tasks, ensuring timely processing of critical tasks and stable system operation.

[0176] The load balancing and fault tolerance mechanism unit monitors the load status of each module in the system, dynamically allocates tasks through the load balancing algorithm, and automatically activates the fault tolerance mechanism when a fault occurs to ensure the high availability of the system.

[0177] The performance monitoring and feedback unit monitors the performance data of each functional module in real time and adjusts the scheduling strategy through the feedback mechanism to achieve adaptive performance optimization.

[0178] The global optimization decision-making unit analyzes the system's operating status from a global perspective and automatically generates system performance optimization strategies by combining machine learning algorithms to ensure that all modules work together to achieve the optimal state.

[0179] The adaptive adjustment mechanism unit automatically adjusts the coordination strategy between modules according to changes in the system environment and tasks, ensuring that the system continues to operate efficiently and optimizes resource utilization under different working conditions.

[0180] In summary, the system collaborative control module achieves efficient system operation and performance optimization through components such as resource scheduling and 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 receives appropriate resource allocation; by employing efficient communication protocols and data transmission optimization algorithms, it reduces latency and bandwidth consumption; by using load balancing algorithms and fault tolerance mechanisms, it guarantees high system availability; by monitoring performance data in real time and adjusting scheduling strategies, it achieves adaptive performance optimization; by combining machine learning algorithms to generate system performance optimization strategies, it ensures that each module works collaboratively to the optimal state; and by automatically adjusting the coordination strategies between modules, it ensures that the system continues to operate efficiently under different working conditions. Combining these functions, the system collaborative control module significantly improves the overall system performance and response speed, ensuring system stability and efficiency.

[0181] Furthermore, the system monitors the load of each module, dynamically allocates tasks using a load balancing algorithm, and automatically activates a fault-tolerance mechanism in case of a failure, including the following steps:

[0182] Use monitoring tools to collect resource usage data of each module in real time, and set thresholds to trigger alarms;

[0183] Based on load data, a dynamic load balancing algorithm is used to adjust task allocation, ensuring the rational use of resources and avoiding overload;

[0184] When a module becomes overloaded, the system automatically migrates the task to a less loaded module to ensure that the task is completed without interruption.

[0185] In the event of a fault, a redundant module is automatically activated to take over the task, ensuring that system operation is not affected.

[0186] Record fault logs and trigger alarms to ensure that maintenance personnel can respond and resolve issues in a timely manner.

[0187] In summary, by monitoring the load of each module in real time and employing a dynamic load balancing algorithm to adjust task allocation, the system ensures rational resource utilization and avoids overload. When a module becomes overloaded, the system automatically migrates tasks to a less loaded module to ensure uninterrupted operation. In the event of a fault, a redundant module is automatically activated to take over the tasks, ensuring unaffected system operation. The system also records fault logs and triggers alarms, ensuring that maintenance personnel can respond and resolve issues promptly. Overall, this mechanism significantly improves system stability and high availability, ensuring efficient operation of each module under various working conditions.

[0188] Furthermore, based on a global perspective, the system's operational status is analyzed, and machine learning algorithms are used to automatically generate system performance optimization strategies to ensure that all modules work together to achieve optimal performance. This includes the following steps:

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

[0190] Based on the collected data, assess the overall health of the system, build a global state model, analyze the resource dependencies and synergies between modules, and identify potential performance bottlenecks and synergy issues.

[0191] We use machine learning algorithms for feature engineering to extract key features that are crucial to system performance optimization, and perform data cleaning and preprocessing to ensure data quality.

[0192] Based on historical performance data and global state models, machine learning algorithms are trained to automatically learn and predict which configuration and scheduling strategies can improve system performance.

[0193] Based on the trained machine learning model, the optimal system performance optimization strategy is automatically generated. By adjusting the coordination and resource allocation between modules, the system state is optimized for different workloads and operating environments.

[0194] The generated optimization strategy is applied to the system, and its effect is monitored in real time. The actual effect of the optimization strategy is evaluated using a performance feedback mechanism, and adjustments and optimizations are made continuously based on the feedback.

[0195] In summary, by analyzing the system's operational status from a global perspective and combining this with machine learning algorithms to automatically generate system performance optimization strategies, we ensure that all modules work together to achieve optimal performance. Specific steps include collecting system operational data, assessing the overall health status, constructing a global state model, performing feature engineering using machine learning algorithms, training and predicting optimization strategies, and applying and monitoring the optimization effects in real time. Through these steps, the system can dynamically adjust resource allocation and scheduling strategies, continuously optimize the synergy between modules, and significantly improve the overall performance and operational efficiency of the system.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to 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, Includes the following modules: The intelligent voice acquisition and enhancement module achieves high-quality voice signal acquisition and preprocessing in complex environments through adaptive noise suppression and multi-channel signal fusion technology; The intelligent voice acquisition and enhancement module includes the following components: The adaptive noise suppression unit analyzes environmental noise in real time and adjusts the noise suppression strategy through an adaptive algorithm to improve the clarity of the speech signal. The multi-channel signal fusion unit collects speech signals through multiple microphone arrays and fuses data from different channels to improve the spatial resolution and acquisition accuracy of the signal. The signal spectrum reconstruction unit performs spectrum analysis on the speech signal and improves the frequency response range of the speech through reconstruction algorithms to ensure high-quality speech output. The multimodal feature extraction module, based on a hierarchical feature extraction architecture, dynamically extracts and fuses multi-dimensional learned feature information, and completes phoneme-level feature analysis and boundary segmentation, while also being responsible for feature normalization and optimization. The multimodal feature extraction module includes the following components: The video feature extraction unit extracts facial movement and lip features from video signals through visual input, helping to identify language information in multimodal signals; The feature fusion and enhancement unit dynamically fuses data from audio, video, and other sensors, and enhances the complementarity of information from different modalities through a deep learning model, thereby improving the effectiveness of multi-dimensional features. The deep speech recognition module utilizes a multilingual hybrid acoustic model and attention-enhanced sequence modeling to achieve high-precision speech recognition based on normalized features. The deep speech recognition module includes the following components: The multilingual hybrid acoustic unit achieves 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 employs a multi-head self-attention mechanism to capture long-distance speech temporal dependencies and strengthens the contextual association modeling of ambiguous speech segments; The semantic alignment decoder unit dynamically fuses acoustic and linguistic features based on attention weights, performs semantic-level alignment on basic phoneme alignment, generates text sequences frame by frame, and integrates the semantic coherence of the dialogue scene. The intelligent assessment engine module constructs a multi-level scoring standard system to achieve real-time, multi-dimensional assessment of learners' language performance, and is responsible for generating assessment results, personalized feedback content, error correction analysis and optimization suggestions; The personalized learning management module, based on a hybrid reasoning mechanism of deep learning and knowledge graphs, integrates evaluation results to dynamically plan the optimal learning path for learners and manage their learning progress, while also providing learning effect prediction and intervention suggestions. The personalized learning management module includes the following components: a knowledge graph reasoning unit, which combines knowledge graph technology to reason about the relationships and dependencies between knowledge points, dynamically planning the learner's optimal learning path, and ensuring the logical coherence and effectiveness of the content; The multi-source reasoning coordinator unit integrates the results of deep learning reasoning and knowledge graph reasoning to generate a unified learning path decision. The learning outcome prediction unit predicts future learning outcomes based on learners' historical data and current performance, identifies potential learning bottlenecks, and intervenes in advance. The learning interaction experience module is responsible for the visualization of learning feedback, multimodal human-computer interaction design, and user interface optimization. It displays real-time feedback and suggestions generated by the intelligent evaluation engine to ensure the intuitive and effective delivery of learning guidance and error correction suggestions. 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 adaptive optimization of the overall system performance. The system collaborative control module includes the following components: The resource scheduling and management unit dynamically schedules computing resources based on system load and module requirements to ensure that each functional module receives appropriate resource allocation in order to optimize system performance and response speed. The inter-module communication optimization unit reduces latency and bandwidth consumption between modules through efficient communication protocols and data transmission optimization algorithms, ensuring efficient interconnection of modules and improving overall system performance. The global optimization decision-making unit analyzes the system's operating status from a global perspective and automatically generates system performance optimization strategies by combining machine learning algorithms, ensuring that all modules work together to achieve the optimal state.

2. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The intelligent voice acquisition and enhancement module includes the following components: The signal enhancement and echo cancellation unit integrates signal enhancement and echo cancellation functions. It optimizes speech quality and removes interference through gain control, dynamic range compression technology and echo cancellation algorithm. The voice activity detection unit accurately identifies voice signals by detecting the start and end of voice activities, thus avoiding irrelevant noise from affecting subsequent processing. The data preprocessing and transmission unit reduces noise, removes irrelevant information, and optimizes the data transmission format of the enhanced speech signal to ensure that the signal is effectively transmitted to the next processing module.

3. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The multimodal feature extraction module includes the following components: The high-order spectral feature extraction unit extracts deep frequency domain and time domain features from the preprocessed speech signal to provide audio information for subsequent analysis. The speech dynamic feature analysis unit analyzes the temporal features of speech signals, providing support for accurate phoneme-level analysis and boundary segmentation; The phoneme analysis and alignment unit combines phoneme-level temporal features and basic phoneme alignment information to accurately analyze the temporal features of each phoneme and perform boundary segmentation to ensure speech recognition accuracy. The feature normalization and optimization unit standardizes the extracted features and selects the most representative and discriminative features according to the needs of the speech recognition task, reducing redundant information and improving 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-dependent feature transformation on the normalized features to adapt them to the model input; The 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. The dynamic vocabulary prediction unit dynamically adjusts the probability distribution of the output vocabulary based on 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 output readability and system robustness.

5. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The intelligent evaluation engine module includes the following components: The language performance assessment unit analyzes learners' pronunciation, grammar, and intonation, and provides a score based on phoneme and sentence structure features to assess the accuracy of their language performance. The speech quality analysis unit, combined with the quality metadata provided by the signal enhancement and echo cancellation unit, analyzes the clarity, fluency, and stress characteristics of learners' speech, providing a quantitative assessment of the quality of language expression. The grammar and semantic understanding unit performs grammatical analysis and semantic understanding based on learners' language input, identifies grammatical errors and semantic deviations in sentences, and ensures the correctness of language content; The progress and performance comparison unit dynamically assesses learners' progress by comparing their current performance with their historical learning data, and identifies weaknesses and areas that need improvement in their learning. The structured feedback generation unit generates standardized JSON format feedback data based on 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 learners' mistakes, 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, characterized in that, The personalized learning management module includes the following components: The learner profile building unit constructs detailed learner profiles by analyzing learners' learning behaviors, preferences, and historical performance, providing basic data for personalized recommendations and learning path planning; The deep learning inference engine unit uses deep learning algorithms to analyze learners' learning progress and performance, and adjusts the learning path planning in real time to ensure personalized adaptation of learning content. The learning progress management unit tracks learners' progress in real time and automatically adjusts the difficulty of tasks and challenges to ensure that the learning burden is appropriate and matches the learner's ability. The learning outcome prediction unit predicts future learning outcomes based on learners' historical data and current performance, identifies potential learning bottlenecks, and intervenes in advance. The intervention suggestion generation unit automatically generates personalized intervention suggestions based on the learning outcome prediction results and feedback from the assessment engine. These suggestions include adjusting the learning plan, recommending supplementary materials, and providing review suggestions to help learners overcome difficulties. The learning plan adjustment unit automatically adjusts personalized learning plans based on learners' real-time feedback and performance, optimizing learning goals and task arrangements to improve learning outcomes.

7. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The learning interaction experience module includes the following components: The visual feedback display unit receives and parses the structured feedback data generated by the assessment engine, and is responsible for displaying learners' learning progress, scores and feedback, ensuring that learners can intuitively see their learning outcomes; 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 efficiently with the system through voice or text input. The multimodal feedback fusion unit integrates various feedback formats from the assessment engine to enhance learners' immersion and interactive experience, making 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, thereby improving learners' operational efficiency. The learning guidance showcases personalized learning suggestions and guidance generated based on assessment results, helping learners optimize their learning strategies, adjust their learning priorities, and improve their learning efficiency. The interactive guidance and assistance unit provides real-time guidance and help during the learning process, using interactive prompts and virtual assistant functions to help learners resolve questions and guide their learning direction.

8. The language learning assistance application system based on speech recognition according to claim 1, characterized in that, The system collaborative control module includes the following components: The task allocation and priority control unit intelligently allocates computing tasks and adjusts task priorities based on the urgency and complexity of module tasks, ensuring timely processing of critical tasks and stable system operation. The load balancing and fault tolerance mechanism unit monitors the load status of each module in the system, dynamically allocates tasks through the load balancing algorithm, and automatically activates the fault tolerance mechanism when a fault occurs to ensure 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 the feedback mechanism to achieve adaptive performance optimization. The adaptive adjustment mechanism unit automatically adjusts the coordination strategy between modules according to changes in the system environment and tasks, 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, characterized in that, The system monitors the load of each module, dynamically allocates tasks using a load balancing algorithm, and automatically activates a fault tolerance mechanism in case of a failure. This includes the following steps: Use monitoring tools to collect resource usage data of each module in real time, and set thresholds to trigger alarms; Based on load data, a dynamic load balancing algorithm is used to adjust task allocation, ensuring the rational use of resources and avoiding overload; When a module becomes overloaded, the system automatically migrates the task to a less loaded module to ensure that the task is completed without interruption. In the event of a fault, a redundant module is automatically activated to take over the task, ensuring that system operation is not affected. Record fault logs and trigger alarms to ensure that maintenance personnel can respond and resolve issues in a timely manner.

10. The language learning assistance application system based on speech recognition according to claim 8, characterized in that, Based on a global perspective, the system's operational status is analyzed, and machine learning algorithms are used to automatically generate system performance optimization strategies to ensure that all modules work together to achieve optimal performance. This includes the following steps: Collect system operation data from various modules, including resource usage, task processing speed, and response time, to provide a comprehensive data foundation for subsequent analysis; Based on the collected data, assess the overall health of the system, build a global state model, analyze the resource dependencies and synergies between modules, and identify potential performance bottlenecks and synergy issues. We use machine learning algorithms for feature engineering to extract key features that are crucial to system performance optimization, and perform data cleaning and preprocessing to ensure data quality. Based on historical performance data and global state models, machine learning algorithms are trained to automatically learn and predict which configuration and scheduling strategies can improve system performance. Based on the trained machine learning model, the optimal system performance optimization strategy is automatically generated. By adjusting the coordination and resource allocation between modules, the system state is optimized for different workloads and operating environments. The generated optimization strategy is applied to the system and its effect is monitored in real time. The actual effect of the optimization strategy is evaluated using a performance feedback mechanism, and adjustments and optimizations are made continuously based on the feedback.

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