English listening oral deep training system capable of shortening semantic decoding delay
Through the three-stage training architecture driven by acoustic features, the problems of semantic decoding delay and neural pathway fragmentation in English listening teaching are solved, rapid semantic understanding and context logic reconstruction are achieved, and the efficiency of English listening oral training and scene transfer capabilities are improved.
Patent Information
- Application Number
- CN202510767782.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-10
- Publication Date
- 2025-08-12
AI Technical Summary
There are problems in existing English listening teaching such as semantic decoding delay caused by grammatical locality, inefficient neural pathway coordination caused by input and output cleavage, and ineffective repetition accumulation, which is difficult to improve learners' application ability in real scenarios.
A three-stage training architecture driven by acoustic features is adopted, including speech input, semantic understanding, speech output, dialogue management, acoustic feature analysis, logical network reconstruction and spoken retelling verification. Through acoustic feature analysis and logical network reconstruction, a fast conversion path from auditory signals to semantic understanding is established, realizing dynamic reconstruction of contextual logical relationships and improving the synergistic efficiency of neural pathways.
The semantic decoding delay is shortened, the conversion path efficiency of auditory signals to semantic comprehension is improved, the dynamic reconstruction ability of contextual logical relationships is enhanced, the neural pathway coordination efficiency of auditory comprehension and spoken expression is improved, the learning cycle is shortened, and the accuracy of complex dialogue logic restoration is improved.
Smart Images
Figure CN120472891A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of English listening teaching, and in particular to an English listening and speaking in-depth training system that shortens semantic decoding delay. Background Art
[0002] English listening instruction currently faces multiple technical bottlenecks that hinder the improvement of real-world application capabilities. The limitations of grammar-based training have led to an over-reliance on grammatical rules to deconstruct audio signals, forcing learners to transform real-time listening into delayed translation, resulting in a response speed significantly lower than the demands of real-world language interaction. Furthermore, mainstream curriculum design is plagued by fragmentation, focusing on isolated word / sentence recognition while neglecting contextual and logical cohesion training. This makes it difficult for learners to construct a complete semantic layer and predict the flow of information.
[0003] Furthermore, existing products adopt an input-output split model, splitting listening and speaking into independent links (such as mechanical shadowing followed by discrete dialogue practice), resulting in low efficiency in the coordination of neural pathways between auditory comprehension and oral expression; the deeper contradiction lies in the accumulation of ineffective repetition, emphasizing mechanical repetition rather than semantic processing based on deep analysis of acoustic features, and the stimulation of the brain's language center remains at the surface memory reinforcement, causing an imbalance between cognitive load and training effectiveness, and ultimately forming a technical deadlock of "high training intensity-low scene transfer".
[0004] Therefore, those skilled in the art provide an English listening and speaking deep training system that shortens semantic decoding delay to solve the problems raised in the above background technology. Summary of the Invention
[0005] In response to the shortcomings of the existing technology, the present invention provides an English listening and speaking deep training system that shortens the semantic decoding delay. Through a three-stage training architecture driven by acoustic features, it constructs a progressive technical closed loop of acoustic feature analysis, logic network reconstruction, and oral retelling verification, breaking through the inherent defects of the discretization of the technical framework in traditional sub-item training, shortening the conversion path from auditory signals to semantic understanding, realizing the dynamic reconstruction capability of contextual logical relationships, and establishing an oral retelling response mechanism based on deep semantic processing. It solves the problem that existing products adopt an input-output split mode, splitting listening and speaking into independent links, resulting in low efficiency of the neural pathway coordination between auditory comprehension and oral expression.
[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:
[0007] An English listening and speaking deep training system that shortens semantic decoding delay includes a speech input module, a semantic understanding module, a speech output module, a dialogue management module, an acoustic feature analysis module, a logic network reconstruction module, and a spoken language retelling verification module.
[0008] The voice input module uses high-precision voice recognition technology, which can accurately recognize voice inputs of various English accents and different speaking speeds;
[0009] The semantic understanding module uses natural language processing technology to perform semantic analysis and understanding on the recognized text;
[0010] The speech output module converts the system-generated answers or training content into natural and fluent speech output based on text-to-speech synthesis technology;
[0011] The dialogue management module is responsible for coordinating the interaction between speech input, semantic understanding and speech output, and managing the dialogue process;
[0012] The acoustic feature analysis module is used to identify the acoustic features of various sentence patterns and extract the main information of the sentence;
[0013] The logic network reconstruction module is used to match multiple types of logical relationship acoustic labels and construct a semantic network graph;
[0014] The spoken language paraphrase verification module calls a parameterized paraphrase rule library and outputs personalized paraphrase content based on the semantically reorganized spoken language.
[0015] Furthermore, the voice input module can also utilize a deep learning model, such as a voice recognition model based on a Transformer architecture, to perform real-time recognition of the input voice and convert it into text information.
[0016] Furthermore, the semantic understanding module can also use word vector models, syntactic analyzers and semantic role labeling tools to decompose sentences into semantic units and establish semantic relationships in order to quickly understand the meaning of the sentence.
[0017] Furthermore, the speech output module can also adopt a deep learning speech synthesis model, such as WaveNet or Tacotron, and generate high-quality speech based on the input text.
[0018] Furthermore, the dialogue management module can also decide what kind of answer to generate based on the user's input and the system status, and control the rhythm and logic of the entire dialogue.
[0019] Furthermore, the acoustic feature analysis module can also establish acoustic recognition templates for main sentence patterns, such as covering SVO structures, existential sentences, and interrogative inversion sentences, and predict syntactic structures based on acoustic features, such as long pauses and rising intonation to identify the beginning of clauses, breaking through the traditional grammar translation path, triggering keyword positioning through stress distribution and connected reading and weak reading patterns, and training students to screen out branch information such as adverbial clauses within 0.5-1 seconds and directly extract the main predicate core.
[0020] Furthermore, the logic network reconstruction module can also construct a multimodal tag library of logical relationships, such as causal chain, contrast, and concession, and define the acoustic fingerprint corresponding to each logic, such as the stress on the first word of the causal chain, the subsequent accelerated speech speed, and the steep drop in pitch of contrast logic. It also adopts auditory-visual dual-channel intensive training to establish a conditioned reflex logical reasoning network through the sound feature recognition of logical connectives and flow chart mapping.
[0021] Furthermore, the oral retelling verification module abandons the mechanical reproduction mode of shadow reading and adopts a parameterized retelling rule library, such as active-passive conversion, synonym replacement, and logical word transcription, to forcibly activate active expression after deep understanding, and requires students to complete oral output through self-semantic reorganization based on the main information and logical relationship diagram analyzed in the first two stages.
[0022] The present invention provides an English listening and speaking in-depth training system that shortens semantic decoding delay. It has the following beneficial effects:
[0023] 1. The present invention provides an English listening and speaking deep training system that shortens the semantic decoding delay. It optimizes the information decoding path, shifts from grammatical filtering to auditory feature anchoring, reconstructs the signal processing mechanism, and strips off redundant grammatical analysis to shorten the semantic extraction delay, thereby shortening the conversion path from auditory signals to semantic understanding.
[0024] 2. The present invention provides an English listening and speaking in-depth training system that shortens the semantic decoding delay. Through dynamic modeling of semantic networks, fragmented information is integrated into a logical relationship map to solve the problem of context connection faults. Causal / transition marker word classification is used to strengthen logical reasoning and realize the dynamic reconstruction capability of contextual logical relationships.
[0025] 3. The present invention provides an English listening and speaking deep training system that shortens the semantic decoding delay. It uses neural pathway collaborative verification, upgrades passive reception to active reconstruction, drives a cross-modal closed loop between auditory input and oral output, completes neural link reinforcement through oral retelling after self-semantic processing, and establishes an oral retelling response mechanism based on deep semantic processing. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 This is a schematic diagram of the structure of the English listening and speaking in-depth training system of the present invention;
[0027] Figure 2 This is a three-stage progressive training flow chart of "acoustic feature analysis-logic network reconstruction-oral paraphrase verification" of the present invention. DETAILED DESCRIPTION
[0028] The following will be combined with the drawings in the specific embodiments of the present invention to clearly and completely describe the technical solutions in the specific embodiments of the present invention. Obviously, the specific embodiments described are only part of the specific embodiments of the present invention, rather than all the specific embodiments. Based on the specific embodiments of the present invention, all other specific embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0029] Example:
[0030] like Figure 1 As shown, an embodiment of the present invention provides an English listening and speaking deep training system that shortens semantic decoding delay, including a speech input module, a semantic understanding module, a speech output module, a dialogue management module, an acoustic feature analysis module, a logic network reconstruction module, and a spoken language paraphrase verification module;
[0031] The voice input module uses high-precision voice recognition technology, which can accurately recognize voice inputs of various English accents and different speaking speeds;
[0032] Use deep learning models, such as speech recognition models based on the Transformer architecture, to recognize input speech in real time and convert it into text information.
[0033] The semantic understanding module uses natural language processing technology to perform semantic analysis and understanding on the recognized text;
[0034] Use word vector models, syntactic analyzers, and semantic role labeling tools to break sentences into semantic units and establish semantic relationships to quickly understand the meaning of sentences.
[0035] The speech output module converts the system-generated answers or training content into natural and fluent speech output based on text-to-speech synthesis technology;
[0036] Use deep learning speech synthesis models, such as WaveNet or Tacotron, to generate high-quality speech based on the input text.
[0037] The dialogue management module is responsible for coordinating the interaction between speech input, semantic understanding and speech output, and managing the dialogue process;
[0038] Based on the user input and the system status, it determines what kind of response to generate and controls the rhythm and logic of the entire conversation.
[0039] The acoustic feature analysis module is used to identify the acoustic features of various sentence patterns and extract the main information of the sentence;
[0040] Then, an acoustic recognition template for the main sentence pattern is established, covering, for example, SVO structures, existential sentences, and interrogative inversion sentences. The syntactic structure is predicted based on acoustic features, such as long pauses and rising intonation to identify the beginning of clauses. This breaks through the traditional grammar translation path, triggers keyword positioning through stress distribution and connected reading and weak reading patterns, and trains students to screen out branch information such as adverbial clauses within 0.5-1 seconds and directly extract the main predicate core.
[0041] The logic network reconstruction module is used to match multiple types of logical relationship acoustic labels and construct a semantic network graph;
[0042] Construct a multimodal tag library of logical relationships, such as causal chain, contrast, and concession, and define the acoustic fingerprint corresponding to each logic, such as the stress on the first word of the causal chain, the subsequent accelerated speaking speed, and the steep drop in pitch of contrast logic. Use auditory-visual dual-channel intensive training to establish a conditioned reflex logical reasoning network through the sound feature recognition of logical connectives and flow chart mapping.
[0043] The spoken language paraphrase verification module calls a parameterized paraphrase rule library and outputs personalized paraphrase content based on the semantically reorganized spoken language;
[0044] Abandoning the mechanical reproduction mode of shadow reading, a parameterized retelling rule library is adopted, such as active-passive conversion, synonym replacement, and logical word transcription, to forcibly activate active expression after deep understanding, and require students to complete oral output through self-semantic reorganization based on the main information and logical relationship diagram analyzed in the first two stages.
[0045] like Figure 2 As shown, the present invention adopts a three-stage progressive training method of "acoustic feature analysis - logic network reconstruction - oral retelling verification", with acoustic feature driving, logic graph reconstruction, and neural pathway closed loop as core technical breakthroughs, forming the following characteristics:
[0046] 1. Revolutionary improvement in hearing processing efficiency and accuracy
[0047] Acoustic filtering technology (acoustic feature-driven stage) is based on the acoustic template of the main sentence structure and directly triggers syntactic prediction through stress, connected reading, and pause features. It is expected to reduce the word-by-word decoding time of traditional grammar translation from 3-4 seconds to 0.8-1.2 seconds, and improve the efficiency of extracting core semantics of listening.
[0048] Logical markup enhancement technology (logical graph reconstruction stage) uses acoustic logical fingerprints (such as "first word stress + faster speech speed" of the causal chain) to restore the logical coherence of the context;
[0049] 2. Synergistic reinforcement of input-output neural pathways
[0050] Dynamic paraphrase technology (verification phase of oral paraphrase) forcibly activates language production neural circuits through parametric semantic reorganization (such as synonymous conversion and logical transcription). fMRI monitoring shows that the co-activation of Broca's area (language production) and Wernicke's area (language comprehension) increased by 42%, breaking through the problem of "understanding but not speaking" in the disconnected neural circuit.
[0051] Creative value: Create a brain science adaptation path of "acoustic decoding-logical modeling-dynamic translation" to achieve a seamless closed loop of language input and output.
[0052] 3. Breakthroughs in adaptability to complex scenarios and technical compatibility
[0053] Enhanced robustness of long-tail speech: The semantic capture completeness of non-standard accents is improved, effectively solving the industry pain point of insufficient scene migration capabilities.
[0054] 4. Building barriers to differentiated market competitiveness
[0055] A solution that integrates the entire "acoustic features-logical reconstruction-neural closed loop" chain in the English training field is expected to shorten the learning cycle by 37% compared to traditional sub-item training.
[0056] Through standardized modules (sentence template library, logical tag library, and retelling rule library), a replicable teaching product matrix is formed, which can be embedded in online education platforms such as smart listening apps and interactive oral systems.
[0057] The solution of the present invention systematically overcomes the technical contradiction of the imbalance between auditory processing efficiency and scene transfer ability in traditional listening teaching. It is expected to shorten the listening response speed in a real context to 0.8-1.2 seconds (75% of the level of native speakers), while increasing the accuracy of restoring complex dialogue logic to 2.3 times the baseline value, achieving the evolution of listening ability from "decoding delay" to "intuitive response".
[0058] Although specific embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these specific embodiments without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. An English listening and speaking deep training system that shortens semantic decoding delay, characterized by: It includes speech input module, semantic understanding module, speech output module, dialogue management module, acoustic feature analysis module, logic network reconstruction module and oral retelling verification module; The voice input module uses high-precision voice recognition technology, which can accurately recognize voice inputs of various English accents and different speaking speeds; The semantic understanding module uses natural language processing technology to perform semantic analysis and understanding on the recognized text; The speech output module converts the system-generated answers or training content into natural and fluent speech output based on text-to-speech synthesis technology; The dialogue management module is responsible for coordinating the interaction between speech input, semantic understanding and speech output, and managing the dialogue process; The acoustic feature analysis module is used to identify the acoustic features of various sentence patterns and extract the main information of the sentence; The logic network reconstruction module is used to match multiple types of logical relationship acoustic labels and construct a semantic network graph; The spoken language paraphrase verification module calls a parameterized paraphrase rule library and outputs personalized paraphrase content based on the semantically reorganized spoken language.
2. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The voice input module can also use a deep learning model, such as a voice recognition model based on a Transformer architecture, to recognize the input voice in real time and convert it into text information.
3. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The semantic understanding module can also use word vector models, syntactic analyzers and semantic role labeling tools to decompose sentences into semantic units and establish semantic relationships in order to quickly understand the meaning of the sentence.
4. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The speech output module can also adopt a deep learning speech synthesis model, such as WaveNet or Tacotron, and generate high-quality speech based on the input text.
5. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The dialogue management module can also determine what kind of response to generate based on the user's input and the system's status, and control the rhythm and logic of the entire dialogue.
6. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The acoustic feature analysis module can also establish acoustic recognition templates for main sentence patterns, such as covering SVO structures, existential sentences, and interrogative inversion sentences, and predict syntactic structures based on acoustic features, such as identifying the start of clauses by long pauses and rising intonation, breaking through the traditional grammatical translation path, triggering keyword positioning through stress distribution and connected reading and weak reading patterns, and training students to screen out branch information of adverbial clauses within 0.5-1 seconds and directly extract the core of the main predicate.
7. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The logic network reconstruction module can also construct a multimodal tag library of logical relationships, such as causal chains, contrast, and concession, and define the acoustic fingerprints corresponding to each logic, such as the stress on the first word of the causal chain, the accelerated subsequent speech speed, and the steep drop in pitch for contrast logic. It also uses auditory-visual dual-channel intensive training to establish a conditioned reflex logical reasoning network through the sound feature recognition of logical connectives and flow chart mapping.
8. The English listening and speaking deep training system for shortening semantic decoding delay according to claim 1 is characterized in that: The oral retelling verification module abandons the mechanical reproduction mode of shadow reading and adopts a parameterized retelling rule library, such as active-passive conversion, synonym replacement, and logical word transcription, to forcibly activate active expression after deep understanding, and requires students to complete oral output through self-semantic reorganization based on the main information and logical relationship diagram analyzed in the first two stages.