A cross-lingual adaptive multimodal speech simulation interaction and context correction processing method

Abstract

The application discloses a kind of cross-language adaptive multimodal speech simulation interaction and context correction processing method, including multimodal interaction data acquisition and language self-adaptive identification, time sequence context modeling, cross-language semantic preliminary analysis and deviation pre-judgment, dynamic context correction processing, high naturalness speech simulation synthesis and real-time interaction output and model iteration optimization.The application can cover multi-national mainstream language, minority language and dialect interaction scene by constructing multilingual self-adaptive identification and adaptation mechanism, solve the problem of narrow cross-language adaptation range and low adaptation precision of traditional technology, realize full-scene multilingual real-time adaptive interaction, form global context chain by building time sequence context modeling system and connecting multi-round dialogue interaction data, completely solve the problem of multi-round cross-language dialogue context fragmentation and logic fault.

Description

Technical Field

[0001] This invention relates to the fields of multimodal voice interaction and cross-lingual artificial intelligence simulation technology, specifically to a cross-lingual adaptive multimodal voice simulation interaction and context correction processing method. Background Technology

[0002] With the rapid popularization of AI voice interaction technology, multilingual voice simulation interaction has been widely applied in scenarios such as cross-border customer service, intelligent translation, and cross-border smart terminal interaction. However, existing multimodal voice interaction technologies suffer from several core shortcomings in practical applications: First, their cross-language adaptability is weak, mostly supporting only fixed language conversion and unable to adapt to multiple minority languages, dialects, and differences in grammar and pronunciation, resulting in extremely poor generalization. Second, the dialogue context is fragmented; traditional voice interaction is mostly single-turn independent recognition interaction, unable to connect with the temporal context of the dialogue, making semantic gaps and logical inconsistencies prone to occur in multi-turn dialogues. Third, the simulated voice effect is stiff; synthesized voice has a monotonous intonation and rigid rhythm, lacking the characteristics of natural spoken language interaction, resulting in a poor human-computer interaction experience. Fourth, the lack of context correction capability means that it cannot dynamically correct semantic deviations, pronunciation recognition errors, and ambiguities based on the real-time dialogue sequence, leading to a significant reduction in the semantic accuracy and fluency of cross-language voice interaction, severely restricting the practical application of multilingual intelligent voice interaction technology.

[0003] In existing technologies, most voice simulation interaction solutions only focus on monolingual speech synthesis or simple bilingual conversion, without building a systematic cross-language adaptive adaptation mechanism. At the same time, they lack temporal context association analysis and dynamic correction logic, and cannot solve core problems such as semantic deviation, pronunciation errors, and context fragmentation in multi-turn cross-language dialogues. They are also unable to meet the needs of high-precision and high-fluency multilingual real-time interaction.

[0004] Therefore, it is necessary to invent a cross-language adaptive multimodal speech simulation interaction and context correction processing method to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a cross-language adaptive multimodal speech simulation interaction and context correction processing method to solve the problems in the above-mentioned technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a cross-language adaptive multimodal speech simulation interaction and context correction processing method, characterized by comprising the following steps: S1. Multimodal interaction data acquisition and language adaptive recognition: Real-time acquisition of user interaction voice signals, text semantics, speech rate and intonation multimodal data, construction of multilingual language feature library, language adaptive matching and recognition of input interaction data, accurate determination of the current interaction language type, and differentiation between mainstream languages ​​and minority languages, standard pronunciation and dialect pronunciation; S2. Temporal Context Modeling: Build a dialogue temporal context association model, store the semantic information, language features, interaction scenarios, and user expression habits data of each round of dialogue in real time, construct a global dialogue temporal context chain, and realize the context association and binding of multi-round dialogues. S3, Cross-language semantic preliminary analysis and deviation prediction: Based on the identified language type, the corresponding language analysis model is called to complete speech-to-text conversion and preliminary semantic analysis. Combined with the temporal context chain, the semantic ambiguity, pronunciation recognition error and cross-language semantic deviation problem in the current sentence are predicted, and deviation prediction results are generated. S4. Dynamic Context Correction Processing: Based on global temporal context information, the predicted semantic deviations, pronunciation errors, and sentence ambiguities are corrected in a graded manner. For minor deviations, local semantic fine-tuning is performed, and for severe deviations, the semantics of the sentence are reconstructed in combination with the historical dialogue context. At the same time, cross-language grammatical adaptation errors are corrected. S5. High-naturalness speech simulation synthesis: Based on the corrected and accurate semantics, combined with the pronunciation rhythm, intonation rules and spoken habits of the corresponding language, the speech synthesis speed, intonation, pauses and stress parameters are adaptively adjusted to generate natural and fluent simulated speech. S6. Real-time interactive output and model iteration optimization: Output simulated voice to complete cross-language interaction, simultaneously record the correction data and interaction effect data of this interaction, iteratively optimize the language adaptation model and context correction model, and improve the accuracy and correction efficiency of subsequent interaction adaptation. Preferably, the multilingual language feature library in S1 pre-stores the speech spectrum features, pronunciation rules, grammatical features, and spoken expression features of mainstream and niche languages ​​of various countries, and supports online addition and updating of language features. The language adaptive matching and recognition adopts a multi-feature fusion similarity matching algorithm, which integrates speech waveform, speech rate, pronunciation habits, and text features to complete accurate language recognition, with the recognition accuracy error controlled within 3%.

[0007] Preferably, the temporal context modeling described in S2 employs a temporal attention mechanism to filter effective contextual data, eliminate invalid and redundant interactive data, and assign weighted values ​​to historical contextual information with different weights, with the weight of recent dialogue context being higher than that of distant dialogue context, thereby achieving accurate association of contextual information and avoiding interference from invalid context in semantic parsing.

[0008] Preferably, the deviation prediction in S3 presets semantic deviation threshold, pronunciation error threshold, and ambiguity threshold. The preliminary analysis results are compared with the temporal context chain and language standard features. If the deviation exceeds the corresponding threshold, it is determined that there is a corresponding deviation. At the same time, the deviation type, deviation location, and deviation severity level are marked.

[0009] Preferably, the graded correction in S4 includes a three-level correction strategy of mild, moderate and severe; the mild correction is word semantic fine-tuning and single pronunciation error correction; the moderate correction is short sentence grammatical adaptation adjustment and local ambiguity resolution; the severe correction is whole sentence semantic reconstruction and cross-language semantic logic error correction.

[0010] Preferably, the speech simulation synthesis described in S5 adopts a multimodal prosodic adaptive synthesis model, which configures the synthesis parameters differently according to the spoken language habits of different languages, simulates the random pauses, intonation fluctuations, and stress shifts of real human speech interaction, and eliminates the mechanical and stiff problem of simulated speech.

[0011] Preferably, the model iterative optimization described in S6 adopts an online incremental learning mode, which can update model parameters without stopping the system. By statistically analyzing multiple dimensions such as interactive semantic accuracy, speech fluency, and deviation correction success rate, the core parameters of the model are optimized in reverse.

[0012] Preferably, the method further includes an interaction anomaly handling step, which monitors abnormal scenarios such as language switching abruptly, voice signal interruption, and semantic parsing failure during cross-language interaction in real time, triggers a context caching protection mechanism for abnormal scenarios, retains the current temporal context data, and continues to complete the interaction after the anomaly is recovered, so as to avoid the loss of context data.

[0013] Preferably, the global dialogue temporal context chain has a scene adaptation and classification function, which can automatically identify different application scenarios such as cross-border customer service, intelligent translation, terminal interaction, and teaching interaction, and match the context correction rules and speech synthesis style of the corresponding scenario.

[0014] Preferably, the multimodal data acquisition includes real-time noise reduction processing, which uses an adaptive noise reduction algorithm to filter out environmental noise, current noise, and background human voice interference, ensuring the purity of the acquired speech signal and providing accurate data support for subsequent language recognition, semantic analysis, and speech synthesis.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention constructs a multilingual adaptive recognition and adaptation mechanism that can cover multiple mainstream languages, minority languages ​​and dialect interaction scenarios, solving the problems of narrow cross-language adaptation range and low adaptation accuracy of traditional technologies, and realizing real-time adaptive interaction in multiple languages ​​across all scenarios. 2. By building a temporal contextual modeling system, multi-turn dialogue interaction data is linked to form a global contextual chain, which completely solves the problems of contextual fragmentation and logical discontinuity in multi-turn cross-language dialogues; 3. Design a layered dynamic context correction logic, which can accurately identify and correct cross-language semantic deviations, pronunciation recognition errors, grammatical adaptation errors and sentence ambiguities, and greatly improve the semantic accuracy of cross-language voice interaction; 4. Based on language characteristics, the speech synthesis parameters are adaptively adjusted to optimize the prosody and intonation features of the simulated speech, solving the problem of the stiffness and mechanicalness of traditional simulated speech, and significantly improving the fluency and naturalness of human-computer interaction; 5. It has the ability to iterate and optimize models, and can continuously optimize and correct accuracy based on real-time interactive data, adapting to the personalized cross-language interactive needs of different users and different scenarios, with strong versatility and inventiveness. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0018] This invention provides, for example Figure 1 The method for cross-language adaptive multimodal speech simulation interaction and context correction processing, as shown, is characterized by the following steps: S1. Multimodal interaction data acquisition and language adaptive recognition: Real-time acquisition of user interaction voice signals, text semantics, speech rate and intonation multimodal data, construction of multilingual language feature library, language adaptive matching and recognition of input interaction data, accurate determination of the current interaction language type, and differentiation between mainstream languages ​​and minority languages, standard pronunciation and dialect pronunciation; S2. Temporal Context Modeling: Build a dialogue temporal context association model, store the semantic information, language features, interaction scenarios, and user expression habits data of each round of dialogue in real time, construct a global dialogue temporal context chain, and realize the context association and binding of multi-round dialogues. S3, Cross-language semantic preliminary analysis and deviation prediction: Based on the identified language type, the corresponding language analysis model is called to complete speech-to-text conversion and preliminary semantic analysis. Combined with the temporal context chain, the semantic ambiguity, pronunciation recognition error and cross-language semantic deviation problem in the current sentence are predicted, and deviation prediction results are generated. S4. Dynamic Context Correction Processing: Based on global temporal context information, the predicted semantic deviations, pronunciation errors, and sentence ambiguities are corrected in a graded manner. For minor deviations, local semantic fine-tuning is performed, and for severe deviations, the semantics of the sentence are reconstructed in combination with the historical dialogue context. At the same time, cross-language grammatical adaptation errors are corrected. S5. High-naturalness speech simulation synthesis: Based on the corrected and accurate semantics, combined with the pronunciation rhythm, intonation rules and spoken habits of the corresponding language, the speech synthesis speed, intonation, pauses and stress parameters are adaptively adjusted to generate natural and fluent simulated speech. S6. Real-time interactive output and model iteration optimization: Output simulated voice to complete cross-language interaction, synchronously record the correction data and interaction effect data of this interaction, iteratively optimize the language adaptation model and context correction model, and improve the accuracy and correction efficiency of subsequent interaction adaptation. The multilingual feature library described in S1 pre-stores speech spectrum features, pronunciation rules, grammatical features, and spoken expression features of mainstream and niche languages ​​from various countries, and supports online addition and updating of language features. The language adaptive matching and recognition uses a multi-feature fusion similarity matching algorithm, integrating speech waveforms, speech rate, pronunciation habits, and text features to achieve accurate language recognition, with an accuracy error controlled within 3%. The temporal context modeling described in S2 uses a temporal attention mechanism to filter effective contextual data, eliminate invalid and redundant interactive data, and assign weighted values ​​to historical contextual information with different weights, giving higher weight to recent dialogue contextual contexts than distant dialogue contexts, achieving accurate association of contextual information and avoiding interference from invalid contextual information in semantic analysis. The deviation prediction described in S3 presets semantic deviation thresholds, pronunciation error thresholds, and ambiguity thresholds, comparing the preliminary analysis results with the temporal context chain and standard language features. If the corresponding threshold is exceeded, a corresponding deviation is determined to exist. The deviation type, location, and severity level are then labeled. The graded correction described in S4 includes three levels of correction strategies: mild, moderate, and severe. Mild correction involves fine-tuning word semantics and correcting single pronunciation errors. Moderate correction involves adjusting short sentence grammar and resolving local ambiguities. Severe correction involves reconstructing the semantics of the entire sentence and correcting cross-language semantic logic errors. The speech simulation synthesis described in S5 employs a multimodal prosodic adaptive synthesis model. Synthesis parameters are configured differently for different languages ​​based on spoken language habits, simulating random pauses, intonation fluctuations, and stress shifts in real-person speech interaction to eliminate the mechanical and stiff problems of simulated speech. The model iterative optimization described in S6 uses an online incremental learning mode, allowing for model parameter updates without system downtime. Multiple indicators, including statistical interaction semantic accuracy, speech fluency, and deviation correction success rate, are used to inversely optimize the core parameters of the model.

[0019] Specifically, the system collects multimodal data such as speech waveforms, speech rate, intonation, and text semantics during users' cross-language interactions in real time. It calls a pre-built multilingual feature library and completes language adaptive recognition through feature similarity matching algorithms, accurately distinguishing different languages ​​such as Chinese and English, Japanese and Korean, and Southeast Asian minor languages. At the same time, it identifies spoken language, dialects, and non-standard pronunciation features to complete the initial adaptation. Secondly, it builds a temporal context caching and modeling module to store the semantic content, interaction scenario, language type, and user expression features of each round of dialogue according to the interaction time sequence, constructing a continuous dialogue context chain to realize the semantic association between the current round of dialogue and the historical dialogue, avoiding context fragmentation caused by single-round interaction. Subsequently, the system performs preliminary analysis of the collected speech data through a cross-language semantic parsing model, outputs the initial semantic results, and combines historical temporal context data to predict problems such as pronunciation recognition errors, cross-language semantic ambiguity, and grammatical adaptation deviations in the current analysis results, and classifies the deviation levels. The method also includes an interaction anomaly handling steps, which monitor language switching abruptly, voice signal interruption, and semantic parsing failure during cross-language interaction in real time. For abnormal scenarios, a context caching protection mechanism is triggered to retain the current temporal context data. After the anomaly is recovered, the interaction is resumed to avoid context data loss. The global dialogue temporal context chain has a scenario adaptation and classification function, which can automatically identify different application scenarios such as cross-border customer service, intelligent translation, terminal interaction, and teaching interaction, and match the context correction rules and speech synthesis style of the corresponding scenario. The multimodal data acquisition includes real-time noise reduction processing, which filters environmental noise, current noise, and background human voice interference through adaptive noise reduction algorithms to ensure the purity of voice signal acquisition and provide accurate data support for subsequent language recognition, semantic parsing, and speech synthesis. Specifically, differentiated correction strategies are implemented for different levels of deviation: for mild semantic deviations and minor pronunciation errors, rapid correction is achieved through local semantic fine-tuning and pronunciation parameter correction; for severe semantic deviations, sentence ambiguities, and grammatical errors, the complete temporal context chain is retrieved, the complete semantics of the sentence are reconstructed, and the corresponding language grammatical rules are adapted to achieve precise correction. After correction, based on the spoken prosody rules of the target language, speech rate, stress, pauses, and intonation parameters are adaptively matched, abandoning the traditional fixed parameter synthesis mode, generating simulated speech that fits natural human-computer interaction, avoiding the problems of stiff and mechanical speech. Finally, simulated speech is output to complete real-time interaction, and correction records, user feedback, and interaction fluency data of this interaction are collected simultaneously. The language recognition model and context correction model parameters are iteratively optimized in reverse to continuously improve the cross-language adaptation accuracy and context correction effect.

[0020] Working principle of this invention: Refer to the instruction manual appendix Figure 1When using this invention, the system first accurately captures interactive information based on a multimodal data acquisition mechanism. While collecting multi-dimensional data such as user voice, speech rate and intonation, and implicit text semantics in real time, it filters out invalid interference signals such as environmental noise, current interference, and background human voices through an adaptive noise reduction algorithm, ensuring the purity of the original interactive data. Relying on a pre-built multilingual feature library covering mainstream languages, minority languages, and dialects, and combined with a multi-feature fusion similarity matching algorithm, it performs multi-dimensional comparisons of the input data's speech spectrum, pronunciation habits, and text features to accurately identify the current interactive language type and pronunciation standardization. This achieves adaptive adaptation to different languages ​​and pronunciation scenarios, solving the problems of narrow language adaptation range and low recognition accuracy inherent in traditional technologies. Secondly, the system constructs a weighted temporal context association model, caching and storing core data such as semantic content, language features, interaction scenarios, and user expression habits for each round of dialogue in real time according to the interaction time sequence. It relies on a temporal attention mechanism to filter effective contextual information and eliminate redundant and invalid data, assigning higher weight to recent contexts and lower weight to distant contexts. This constructs a continuous, accurate, and traceable global dialogue temporal context chain, achieving deep contextual association and binding for multi-round cross-language dialogues. This completely breaks down the contextual fragmentation, semantic discontinuity, and logical inconsistencies caused by traditional single-round voice interaction, providing complete contextual support for subsequent semantic parsing and bias correction. Furthermore, the system calls corresponding language-specific parsing methods based on accurately identified language types. The system analyzes the model to complete speech-to-text conversion and initial semantic parsing. Simultaneously, it cross-compares the initial parsing results with standard language features and the global temporal context chain, based on preset thresholds for semantic deviation, pronunciation errors, and ambiguity. This allows for rapid identification and localization of semantic ambiguity, pronunciation recognition errors, and cross-language grammatical adaptation deviations. The system accurately classifies deviation types and severity levels, predicting deviations in interactive content and providing data support for differentiated correction strategies. Subsequently, the system executes a three-level hierarchical dynamic context correction mechanism based on the deviation prediction results. This mechanism, combined with global temporal context information, achieves precise error correction. For minor deviations such as word-level errors and slight pronunciation mistakes, it employs rapid correction methods such as local semantic fine-tuning and pronunciation parameter correction. For short sentence grammatical errors and local semantic ambiguity... For moderate deviations such as semantic errors, grammatical adaptation and ambiguity resolution are completed. For severe deviations such as whole-sentence semantic errors, cross-language logical conflicts, and serious recognition errors, the complete historical context chain is retrieved to reconstruct the complete semantics of the sentence, adapting to the grammar and expression rules of the corresponding language. This fundamentally solves the semantic distortion problem in cross-language interaction, significantly improving the accuracy of semantic parsing. After semantic correction, the system relies on a multimodal prosodic adaptive synthesis model, abandoning the traditional fixed-parameter synthesis mode. It combines the unique pronunciation prosody, intonation rules, and spoken expression habits of the target language to adaptively and dynamically adjust core parameters such as speech rate, intonation fluctuations, sentence pauses, and word stress in speech synthesis. This simulates the characteristics of natural human spoken interaction, generating simulated speech that is rhythmic, fluent, and without a mechanical feel.To address the issues of stiff, rigid, and poor human-computer interaction in traditional simulated speech, the system outputs highly natural-sounding simulated speech for real-time cross-language interaction. Simultaneously, it implements a full-process data recording and iterative optimization mechanism, synchronously collecting key indicators such as correction data, semantic accuracy, and speech fluency for each interaction. Through online incremental learning, the model achieves continuous, uninterrupted iterative optimization, continuously improving language recognition accuracy and context correction effects. Furthermore, an anomaly protection mechanism protects contextual data in cases of language mutations, signal interruptions, or parsing failures, ensuring continuous interaction. The system can adaptively match correction rules and speech styles for different application scenarios, achieving multi-scenario, personalized, and high-precision cross-language, multimodal speech simulation interaction.

[0021] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cross-language adaptive multimodal speech simulation interaction and context correction processing method, characterized in that, Includes the following steps: S1. Multimodal interaction data acquisition and language adaptive recognition: Real-time acquisition of user interaction voice signals, text semantics, speech rate and intonation multimodal data, construction of multilingual language feature library, language adaptive matching and recognition of input interaction data, accurate determination of the current interaction language type, and differentiation between mainstream languages ​​and minority languages, standard pronunciation and dialect pronunciation; S2. Temporal Context Modeling: Build a dialogue temporal context association model, store the semantic information, language features, interaction scenarios, and user expression habits data of each round of dialogue in real time, construct a global dialogue temporal context chain, and realize the context association and binding of multi-round dialogues. S3, Cross-language semantic preliminary analysis and deviation prediction: Based on the identified language type, the corresponding language analysis model is called to complete speech-to-text conversion and preliminary semantic analysis. Combined with the temporal context chain, the semantic ambiguity, pronunciation recognition error and cross-language semantic deviation problem in the current sentence are predicted, and deviation prediction results are generated. S4. Dynamic Context Correction Processing: Based on global temporal context information, the predicted semantic deviations, pronunciation errors, and sentence ambiguities are corrected in a graded manner. For minor deviations, local semantic fine-tuning is performed, and for severe deviations, the semantics of the sentence are reconstructed in combination with the historical dialogue context. At the same time, cross-language grammatical adaptation errors are corrected. S5. High-naturalness speech simulation synthesis: Based on the corrected and accurate semantics, combined with the pronunciation rhythm, intonation rules and spoken habits of the corresponding language, the speech synthesis speed, intonation, pauses and stress parameters are adaptively adjusted to generate natural and fluent simulated speech. S6. Real-time interactive output and model iteration optimization: Output simulated voice to complete cross-language interaction, simultaneously record the correction data and interaction effect data of this interaction, iteratively optimize the language adaptation model and context correction model, and improve the accuracy and correction efficiency of subsequent interaction adaptation.

2. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The multilingual language feature library mentioned in S1 pre-stores the speech spectrum features, pronunciation rules, grammatical features, and spoken expression features of mainstream and niche languages ​​of various countries, and supports online addition and updating of language features. The language adaptive matching and recognition adopts a multi-feature fusion similarity matching algorithm, which integrates speech waveform, speech rate, pronunciation habits, and text features to complete accurate language recognition, with the recognition accuracy error controlled within 3%.

3. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The temporal contextual modeling described in S2 uses a temporal attention mechanism to filter effective contextual data, eliminate invalid and redundant interactive data, and assign weighted values ​​to historical contextual information with different weights. The weight of recent dialogue context is higher than that of distant dialogue context, so as to achieve accurate association of contextual information and avoid invalid context from interfering with semantic parsing.

4. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The deviation prediction described in S3 presets semantic deviation threshold, pronunciation error threshold, and ambiguity threshold. It compares the preliminary analysis results with the temporal context chain and language standard features. If the deviation exceeds the corresponding threshold, it is determined that there is a corresponding deviation. At the same time, the deviation type, deviation location, and deviation severity level are marked.

5. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The graded correction described in S4 includes three levels of correction strategies: mild, moderate, and severe. The mild correction involves fine-tuning word semantics and correcting single pronunciation errors. The moderate correction involves adjusting short sentence grammar and resolving local ambiguities. The severe correction involves reconstructing the semantics of the entire sentence and correcting cross-language semantic logic errors.

6. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The speech simulation synthesis described in S5 adopts a multimodal prosodic adaptive synthesis model, which configures synthesis parameters according to the differences in spoken language habits of different languages, simulates the random pauses, intonation fluctuations, and stress shifts of real human speech interaction, and eliminates the mechanical and stiff problems of simulated speech.

7. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The model iterative optimization described in S6 adopts an online incremental learning mode, which can update model parameters without stopping the system. It optimizes the core parameters of the model by statistically analyzing multiple dimensions such as interactive semantic accuracy, speech fluency, and deviation correction success rate.

8. The method for cross-language adaptive multimodal speech simulation interaction and context correction processing according to claim 1, characterized in that: The method also includes an interaction anomaly handling step, which monitors abnormal scenarios such as language switching abruptly, voice signal interruption, and semantic parsing failure during cross-language interaction in real time. For abnormal scenarios, a context caching protection mechanism is triggered to retain the current temporal context data. After the anomaly is recovered, the interaction is continued to complete, thus avoiding the loss of context data.

9. The cross-language adaptive multimodal speech simulation interaction and context correction processing method according to claim 1, characterized in that: The global dialogue temporal context chain has a scene adaptation and classification function, which can automatically identify different application scenarios such as cross-border customer service, intelligent translation, terminal interaction, and teaching interaction, and match the context correction rules and speech synthesis style of the corresponding scenario.

10. The cross-language adaptive multimodal speech simulation interaction and context correction processing method according to claim 1, characterized in that: The multimodal data acquisition includes real-time noise reduction processing, which uses an adaptive noise reduction algorithm to filter out environmental noise, current noise, and background human voice interference, ensuring the purity of the acquired speech signal and providing accurate data support for subsequent language recognition, semantic analysis, and speech synthesis.

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