A speech recognition and natural language processing integration method and system

Abstract

The application discloses a speech recognition and natural language processing integrated method and system, and the method comprises the following steps: according to a user's voice signal, context text information and environmental sensor data, multi-modal data fusion is carried out to obtain a fused multi-modal feature vector; the multi-modal feature vector is input into a speech recognition model based on an adaptive deep neural network to carry out speech-to-text processing and obtain a text output; the text output is input into a semantic analysis model based on a graph neural network to carry out context semantic analysis and user intention recognition and obtain a semantic representation of the user intention and a confidence score thereof; according to the semantic representation and the confidence score thereof, a feedback optimization technology based on reinforcement learning is used to dynamically adjust parameters of the speech recognition model and the semantic analysis model to generate a model optimization strategy. By using the embodiment of the application, the precision of speech recognition and the semantic understanding ability of natural language processing can be improved.

Description

Technical Field

[0001] This invention belongs to the field of language processing technology, specifically a method and system for integrating speech recognition and natural language processing. Background Technology

[0002] Speech recognition and natural language processing (NLP) technologies, as important research directions in the field of artificial intelligence, have received widespread attention and rapid development in recent years. With the widespread adoption of smartphones, smart home devices, and various voice assistants, users' demand for voice interaction is increasing, prompting further innovation and integration of related technologies. Traditional speech recognition technologies mainly focus on the process of converting speech signals into text, typically relying on a combination of acoustic and language models. However, these methods often exhibit insufficient robustness when facing environmental noise, variations in user pronunciation, and contextual issues. Especially in real-world scenarios, background noise, accents or dialects, and users' speech habits can severely affect the accuracy of recognition. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for integrating speech recognition and natural language processing, so as to overcome the shortcomings of the prior art and improve the accuracy of speech recognition and the semantic understanding ability of natural language processing.

[0004] One embodiment of this application provides a method for integrating speech recognition and natural language processing, the method comprising:

[0005] Multimodal data fusion is performed based on the user's voice signal, contextual text information, and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector.

[0006] The multimodal feature vectors are input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithms and context-aware technology to obtain text output.

[0007] The text output is input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop inference techniques to obtain the semantic representation of the user intent and its confidence score.

[0008] Based on the semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

[0009] Optionally, the step of performing multimodal data fusion based on the user's voice signal, contextual text information, and environmental sensor data, wherein the multimodal data fusion employs a deep learning-based multimodal alignment network, and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain a fused multimodal feature vector, including:

[0010] Based on the user's voice signal, contextual text information, and environmental sensor data, an edge computing-based data acquisition framework is used to acquire multimodal data in real time. Through an adaptive data cleaning algorithm, noise filtering and format unification are performed on the data to generate a preliminary standardized dataset.

[0011] For the initial standardized dataset, a deep learning-based feature extraction method is used to extract acoustic features from the speech signal and semantic features from the contextual text information. Multi-head attention technology is then used to capture feature representations of different modalities and generate preliminary feature representations.

[0012] For the initial feature representation, a feature fusion method based on cross-modal attention technology is adopted to weight and fuse acoustic features and semantic features. Through context-aware technology, the correlation between acoustic features and semantic features is captured to generate an initial fused feature representation.

[0013] For the initial fusion feature representation, a feature integration method based on matrix factorization is adopted to map the fused features to a unified feature space. Through dynamic weight adjustment technology, the timeliness and consistency of the feature vector are ensured, and the final multimodal feature vector is generated.

[0014] Optionally, the step of inputting the multimodal feature vector into a speech recognition model based on an adaptive deep neural network for speech-to-text processing, wherein the speech recognition model, by combining dynamic noise suppression algorithms and context-aware technology, adjusts recognition parameters in real time to adapt to different environmental noise and user pronunciation habits, and obtains text output, includes:

[0015] For the speech signal in the multimodal feature vector, a preprocessing method based on dynamic noise suppression algorithm is adopted. Combined with the background noise information in the environmental sensor data, the noise suppression parameters are adjusted in real time, and an initial denoised speech signal is generated through adaptive filtering technology.

[0016] For the initial denoised speech signal, a speech recognition model based on an adaptive deep neural network is used. Combined with the semantic features in the contextual text information, speech-to-text processing is performed. Through context-aware technology, the recognition parameters are dynamically adjusted to adapt to the user's pronunciation habits and generate the initial text output.

[0017] For the initial text output, a language model-based correction method is used, which combines contextual semantic features and grammatical rules to optimize the recognition results. Through dynamic threshold adjustment technology, the accuracy and consistency of the recognition results are ensured, and the final text output is generated.

[0018] Optionally, the text output is input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition. The semantic parsing model, through dynamic graph structure learning and multi-hop inference techniques, captures implicit semantic relationships and long-distance dependencies in the text to obtain a semantic representation of the user intent and its confidence score, including:

[0019] For text output, a semantic parsing model based on graph neural networks is adopted, which abstracts the words in the text into nodes in the graph structure and the semantic relationships between words into edges. Through dynamic graph structure learning technology, a preliminary semantic graph structure is generated.

[0020] For the preliminary semantic graph structure, a semantic analysis method based on multi-hop reasoning technology is adopted to capture the implicit semantic relationships and long-distance dependencies in the text. Through attention technology, the selection of reasoning paths is optimized to generate a preliminary semantic representation.

[0021] For the initial semantic representation, an intent recognition method based on a classification model is adopted. Combining contextual semantic features and user historical behavior data, the user intent is identified. Through confidence scoring technology, the accuracy and reliability of intent recognition are calculated, and a semantic representation of the user intent and its confidence score are generated.

[0022] Optionally, based on the semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using a reinforcement learning-based feedback optimization technique. The feedback optimization technique, by combining user interaction feedback data and real-time performance monitoring, generates a model optimization strategy to continuously improve speech recognition accuracy and semantic understanding accuracy, including:

[0023] Based on semantic representation and its confidence score, a user interaction-based feedback data collection method is adopted. Combined with the user's confirmation or correction behavior of the recognition results, user interaction feedback data is generated. Lightweight data caching technology is used to ensure the real-time and continuity of the feedback data.

[0024] The operation status of the speech recognition model and semantic parsing model is analyzed using a real-time performance monitoring method. Combined with recognition accuracy, response time and user satisfaction indicators, a performance monitoring report is generated. Dynamic threshold adjustment technology is used to ensure the accuracy and timeliness of the monitoring results.

[0025] For user interaction feedback data and performance monitoring reports, an optimization strategy generation method based on reinforcement learning is adopted. By combining the parameter space of speech recognition and semantic parsing models, the model parameters are dynamically adjusted. Through a multi-objective optimization algorithm, the recognition accuracy and response time are balanced to generate a preliminary optimization strategy.

[0026] For the initial optimization strategy, a parameter adjustment method based on adaptive learning algorithm is adopted. Combined with real-time performance monitoring data, the parameters of speech recognition model and semantic parsing model are dynamically adjusted. Through feedback correction technology, the model performance is continuously optimized to generate the final optimization strategy.

[0027] Another embodiment of this application provides an integrated speech recognition and natural language processing system, the system comprising:

[0028] The fusion module is used to perform multimodal data fusion based on the user's voice signal, contextual text information, and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector.

[0029] The processing module is used to input the multimodal feature vector into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithm and context awareness technology to obtain text output.

[0030] The recognition module is used to input the text output into a semantic parsing model based on a graph neural network to perform contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop inference techniques to obtain the semantic representation of the user intent and its confidence score.

[0031] The adjustment module is used to dynamically adjust the parameters of the speech recognition model and the semantic parsing model based on the semantic representation and its confidence score using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

[0032] Another embodiment of this application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any of the preceding claims when running.

[0033] Another embodiment of this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described in any of the preceding claims.

[0034] Compared with existing technologies, the present invention provides an integrated method for speech recognition and natural language processing. This method fuses multimodal data based on the user's speech signal, contextual text information, and environmental sensor data to obtain a fused multimodal feature vector. The multimodal feature vector is then input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing, resulting in text output. This text output is then input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition, yielding a semantic representation of the user intent and its confidence score. Based on the semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using reinforcement learning-based feedback optimization techniques to generate a model optimization strategy. This improves the accuracy of speech recognition and the semantic understanding capability of natural language processing. Attached Figure Description

[0035] Figure 1 A hardware structure block diagram of a computer terminal for an integrated method of speech recognition and natural language processing provided in an embodiment of the present invention;

[0036] Figure 2 A flowchart illustrating an integrated method for speech recognition and natural language processing provided in an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of the structure of an integrated speech recognition and natural language processing system provided in an embodiment of the present invention. Detailed Implementation

[0038] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0039] This invention first provides a method for integrating speech recognition and natural language processing, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers.

[0040] The following detailed explanation uses a computer terminal as an example. Figure 1 This is a hardware structure block diagram of a computer terminal for an integrated method of speech recognition and natural language processing provided in an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0041] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any integrated speech recognition and natural language processing method.

[0042] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0043] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When these computer programs are executed by a processor, the processor can perform any integrated speech recognition and natural language processing method.

[0044] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0045] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0046] See Figure 2 The present invention provides an integrated method for speech recognition and natural language processing, which may include the following steps:

[0047] S201, Multimodal data fusion is performed based on the user's voice signal, contextual text information and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector.

[0048] Multimodal data fusion, which integrates user speech signals, contextual text information, and environmental sensor data, means that the system improves the accuracy of speech recognition and natural language processing by aggregating and integrating information from different sources. For example, a user's speech signal contains acoustic features of pronunciation, while contextual text information provides semantic information about the topic being discussed. Furthermore, environmental sensor data (such as noise level, temperature, and lighting) provides the system with additional context to better understand the user's voice and semantics. For instance, in noisy environments, acoustic features may be affected, while contextual information can help the system infer the user's intent. By applying deep learning methods, utilizing multimodal alignment networks and cross-modal attention techniques, the system can extract the correlations between these multimodal information sources, thereby generating richer multimodal feature vectors and laying the foundation for subsequent processing stages.

[0049] This multimodal data fusion approach significantly enhances the performance of speech recognition and natural language processing. Since a single data source can be affected by noise, unclear pronunciation, or incomplete context, it is difficult to accurately understand user intent based solely on speech signals or text information. By integrating multiple data sources, the system can more comprehensively capture the correlation between acoustic features and contextual semantic features in the speech signal, improving its ability to understand user intent in complex contexts. For example, on a noisy street, a user might vaguely express "looking for a restaurant." By combining contextual information (such as the nearest geographical location or previous conversation content), the system can more accurately identify the user's need. Therefore, the final generated multimodal feature vector not only contains the user's vocal features but also contains rich contextual information related to the user's intent, greatly improving the system's intelligence level.

[0050] Specifically, based on the user's voice signal, contextual text information, and environmental sensor data, an edge computing-based data acquisition framework can be used to acquire multimodal data in real time. Through adaptive data cleaning algorithms, noise filtering and format unification of the data can be performed to generate a preliminary standardized dataset.

[0051] In this step, firstly, an edge computing-based data acquisition framework is responsible for collecting voice signals, contextual text information, and environmental sensor data from the user in real time. The advantage of edge computing lies in its ability to process data closer to the data source, thereby reducing latency and improving real-time performance. For example, when the user is outdoors, environmental sensors can capture noise levels, helping the system understand the noise level of the surrounding environment. Simultaneously, the voice signal is also captured and transmitted to the processing unit. Next, adaptive data cleaning algorithms are applied to the collected data, filtering out background noise and irrelevant information, and standardizing data from different formats into a unified dataset, such as converting different audio formats to a uniform sampling rate and bit rate, thus preparing it for subsequent processing.

[0052] This data acquisition and adaptive cleaning approach ensures that the input data to the system is both high-quality and real-time. This not only improves data accuracy but also provides a solid foundation for subsequent speech recognition and semantic parsing. Using edge computing reduces data transmission latency, enabling real-time responses to user voice requests. For example, in applications where only short-duration voice input is allowed, a fast and efficient data cleaning process avoids unnecessary delays, ensuring a smooth user experience. Furthermore, standardized datasets ensure comparability between different modalities, laying the groundwork for further feature extraction and significantly enhancing the effectiveness of multimodal fusion.

[0053] This step first requires establishing a data acquisition framework based on edge computing, aiming to collect multimodal data in real time near the user's device. This data includes the user's voice signals, contextual text information from social media, and sensor data from the environment (such as noise levels and temperature). For example, when a user says "search for nearby coffee shops" while walking, the system captures their voice signal and ambient noise data in real time, and acquires environmental information such as weather conditions and lighting changes through cameras or sensors. Preprocessing at the device level reduces data transmission latency, laying the foundation for subsequent analysis.

[0054] Next, the system uses an adaptive data cleaning algorithm to process the collected data. The algorithm aims to filter out background noise and standardize the data format to ensure data quality. For example, speech signals may contain background noise, and contextual text information may have multiple formats. The adaptive data cleaning algorithm analyzes the quality of this data in real time according to preset standards, automatically identifies and removes low-quality data, and introduces a format conversion mechanism to standardize all data into a unified format, which facilitates subsequent feature extraction and analysis. Finally, after cleaning and format standardization, a preliminary standardized dataset is generated, ensuring the accuracy and usability of the data.

[0055] The significance of this process lies in ensuring the efficiency and accuracy of subsequent feature extraction and analysis steps by acquiring and cleaning multimodal data in real time. Since data processing occurs on edge devices, it reduces latency caused by network transmission, making data processing more timely. For example, in high-noise environments, real-time noise filtering ensures the system captures clear user intent, reducing the risk of misidentification due to background noise, thereby improving the overall performance and accuracy of subsequent speech recognition and semantic analysis.

[0056] For the initial standardized dataset, a deep learning-based feature extraction method is used to extract acoustic features from the speech signal and semantic features from the contextual text information. Multi-head attention technology is then used to capture feature representations of different modalities and generate preliminary feature representations.

[0057] In this stage, the system applies deep learning-based feature extraction methods to extract key acoustic and semantic features from a standardized dataset. For speech signals, models such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) are used to extract acoustic features, such as features based on Mel-frequency cepstral coefficients (MFCCs), which effectively characterize the time-frequency properties of speech. Simultaneously, text information is processed through pre-trained language models (such as BERT and GPT) to extract semantic features of sentences. This process provides a rich feature foundation for multimodal data fusion. Multi-head attention techniques help the system simultaneously focus on different feature dimensions during feature extraction, enhancing the model's expressive power.

[0058] Through deep learning feature extraction, the system can gain a deeper understanding and representation of data from different modalities. This feature extraction not only improves the system's ability to recognize user intent but also captures the complex relationship between acoustic and semantic features. For example, in the speech expression "I want to eat pizza," acoustic features capture the intent of "want to eat," while the semantic features provided by the text information identify the specific object "pizza." The combination of these two aspects significantly improves the accuracy of understanding user needs, providing richer information for subsequent multimodal fusion.

[0059] In this step, deep learning feature extraction is first performed on the initial standardized dataset. Specifically, for speech signals, the system uses convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to extract acoustic features. These features can include Mel-frequency cepstral coefficients (MFCCs) and time-frequency features of the audio signal, effectively characterizing the sonic properties and emotional nuances of speech. Simultaneously, for contextual text information, the system employs a pre-trained language model, such as BERT, to extract semantic features. This allows for a better understanding of the implicit meanings within the text, especially when the textual information is closely related to the meaning of the speech signal.

[0060] Secondly, multi-head attention technology is introduced during feature extraction. By setting multiple attention heads, the system can focus on the importance of different parts of the feature vector when processing features. For example, when analyzing a user's speech signal, one attention head may focus on capturing changes in the user's intonation, while another attention head may focus on the emotional tone of keywords. In this way, the system can generate richer and more multi-dimensional preliminary feature representations. Through the multi-head attention mechanism, combined with acoustic and semantic features, it ensures a comprehensive reflection of the user's intent.

[0061] The significance of this feature extraction process lies in transforming multimodal information into high-dimensional feature representations that can be used for subsequent analysis. This advanced feature extraction can effectively improve the performance of speech recognition and natural language processing. For example, by extracting features from speech signals and text information, the system can more accurately recognize users' voice commands, thereby providing them with more personalized and precise services. The application of multi-head attention can also effectively improve model performance, making the feature representations more powerful.

[0062] For the initial feature representation, a feature fusion method based on cross-modal attention technology is adopted to weight and fuse acoustic features and semantic features. Through context-aware technology, the correlation between acoustic features and semantic features is captured to generate an initial fused feature representation.

[0063] In this step, the system weighted and fused the initial acoustic and semantic features to better capture their relationship within the context. By applying cross-modal attention techniques, the system learns which acoustic features are more important in a specific context and generates a weighted fused feature representation based on these features. For example, when a user says "I want to go to a restaurant," the system can identify "go to a restaurant" as the key intent, and the acoustic and semantic features will be associated through an attention mechanism to generate a more representative fused feature. This feature fusion process provides ample information for subsequent model inputs.

[0064] This feature fusion method significantly improves the accuracy of multimodal understanding. Through associative fusion, the system can utilize not only the temporal information of acoustic features but also the contextual information of semantic features, thereby capturing more complex user intentions. For example, in a noisy environment, when a user says "go to a restaurant," the acoustic features may be interfered with, but after fusion, the semantic features ensure that the system can still accurately identify the user's true intention. This feature fusion makes the system more adaptable to complex environments in practical applications, improving the overall user experience.

[0065] In this step, the system performs feature fusion on the preliminary feature representations. Specifically, a cross-modal attention mechanism is used to weightedly fuse acoustic and semantic features. The system first weights the feature representations for each modality, assigning corresponding weights to different features based on their importance in a specific context. For example, in certain contexts, acoustic features may be more important than semantic features, and vice versa. Through this weighted fusion method, the system can integrate the features from the two modalities into a preliminary fused feature representation.

[0066] Furthermore, through context-aware technology, the system can further capture the deep relationships between acoustic and semantic features. For example, by analyzing the corresponding features of the sound and text when a user says "I'm in a coffee shop," the system can deduce the impact of "coffee shop" on the acoustic features in this scenario, helping to strengthen the interactive relationship between features. This process not only improves the system's ability to understand different sounds and intentions but also provides richer information for subsequent processing.

[0067] The purpose of this feature fusion is to provide a unified and multi-dimensional feature vector for subsequent model inputs, enabling the model to more comprehensively understand user intent and its context. By fusing acoustic and semantic information, the system can more accurately identify user intent, especially in complex interactive scenarios, which helps improve the intelligence level of voice assistants or artificial intelligence systems. For example, in processing multi-turn dialogues, feature fusion ensures that the system captures subtle changes in the conversation, thereby better understanding the user's meaning and making more appropriate responses.

[0068] For the initial fusion feature representation, a feature integration method based on matrix factorization is adopted to map the fused features to a unified feature space. Through dynamic weight adjustment technology, the timeliness and consistency of the feature vector are ensured, and the final multimodal feature vector is generated.

[0069] In this stage, the system uses matrix factorization to integrate the initially fused feature representations, mapping multimodal features to a unified feature space. This process mainly involves decomposing the fused feature matrix to obtain lower-dimensional, more representative feature representations. By mapping features to a common space, the system ensures the comparability of features between different modalities and facilitates subsequent processing steps, such as input to speech recognition and semantic parsing models. Furthermore, dynamic weight adjustment technology ensures that feature vectors maintain consistency and real-time performance across different environments and contexts.

[0070] Through matrix factorization and feature integration, the system effectively improves the quality and expressive power of feature vectors. This approach ensures the timeliness of features, enabling the system to adapt to user pronunciation habits and environmental changes. For example, in complex noisy environments, feature vectors may need to have their weights readjusted to highlight important information, which will help the system perform more accurately and efficiently in speech recognition and intent understanding. Furthermore, a unified feature space makes the subsequent multimodal fusion process smoother, reduces interference between different modalities, and promotes improved speech recognition accuracy.

[0071] In the final step, the system performs matrix factorization on the preliminary fused feature representation. This process mainly involves decomposing the fused feature matrix into several low-dimensional feature matrices. In this way, the system can extract the latent structure and relationships between features. For example, by using singular value decomposition (SVD), the information most important for user intent recognition can be effectively preserved while reducing redundant features. The key to this step is to ensure, through feature confidence analysis, that the mapped features reflect their temporal characteristics and effectiveness in all aspects.

[0072] In this process, dynamic weight adjustment technology will also be introduced to ensure the adaptability and consistency of features across different application scenarios. By analyzing user feedback and performance data in real time, the system can automatically adjust the weights of different features, thereby better balancing the impact of different modal information on user experience and processing accuracy. For example, in some cases, users may rely more on voice input, in which case the system will automatically increase the weight of the corresponding acoustic features in the model.

[0073] The significance of this feature integration process lies in generating a final multimodal feature vector. This vector not only integrates rich acoustic and semantic information but also possesses good timeliness and consistency, ensuring high-quality input for subsequent model processing. Through the generated multimodal feature vector, the system can significantly improve its understanding of user speech and enhance the integration of speech recognition and natural language processing. For example, this feature vector will provide strong support for subsequent speech recognition and intent recognition, enabling the overall intelligent voice assistant or natural language processing system to respond to user needs more flexibly and accurately in complex environments.

[0074] S202, the multimodal feature vector is input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithm and context awareness technology to obtain text output.

[0075] In this step, the system first inputs the fused multimodal feature vectors into a speech recognition model constructed using an adaptive deep neural network (DNN). This model is pre-trained and can adapt to various audio input conditions, especially under varying environmental noise levels or diverse user pronunciation habits. When a user speaks a command in a noisy environment, such as "find nearby restaurants," the system's model can effectively recognize the user's speech and, combined with the previously extracted multimodal features, accurately convert it into text. Through a dynamic noise suppression algorithm, the model can analyze background noise in real time and automatically adjust its recognition parameters to ensure higher recognition accuracy in different environments.

[0076] The significance of this process lies in the fact that by adjusting the recognition parameters during the text-to-speech process in real time, the system can significantly improve the accuracy and robustness of speech recognition. Especially in complex or noisy environments, traditional speech recognition technologies may fail due to background noise. However, by combining dynamic noise suppression techniques, this model not only significantly reduces unnecessary interference but also ensures that the user's core intent is captured. For example, even in a noisy coffee shop environment, the system can successfully recognize the user's request, ensuring that speech recognition not only relies on acoustic features themselves but also effectively combines contextual information to enhance the user experience.

[0077] Specifically, a preprocessing method based on dynamic noise suppression algorithm can be used to preprocess the speech signal in the multimodal feature vector, combined with background noise information in environmental sensor data, to adjust the noise suppression parameters in real time, and generate a preliminary denoised speech signal through adaptive filtering technology.

[0078] In this step, the system first analyzes the input multimodal feature vector, extracts the speech signal portion, and preprocesses it using a dynamic noise suppression algorithm. This algorithm monitors ambient noise in real time and dynamically adjusts noise suppression parameters by combining data from environmental sensors, such as surrounding background volume and frequency. For example, if the system detects that the user is in a noisy coffee shop, the noise suppression algorithm will automatically strengthen the suppression of low-frequency background noise to ensure that the user's speech signal can be clearly captured.

[0079] Furthermore, by introducing adaptive filtering technology, the system can continuously adjust the filter parameters according to real-time changes in the environment. This means that if environmental noise changes over a period of time, the system will automatically update the filter settings, thereby optimizing the processing effect. This real-time feedback mechanism can significantly improve the noise reduction effect, making the processed speech signal clearer and laying a solid foundation for subsequent speech recognition algorithms.

[0080] This process ensures that the signal input to the speech recognition model is as pure as possible, greatly improving the system's accuracy in recognizing user commands. For users, clear voice input means a smoother interactive experience. In noisy environments, the effective application of noise reduction technology ensures that even in complex backgrounds, the system can still capture the essence of voice commands, thereby enhancing the reliability of speech recognition.

[0081] In this implementation process, the speech signal component is first extracted from the multimodal feature vector, and then preprocessed using a dynamic noise suppression algorithm. The core of the dynamic noise suppression algorithm lies in dynamically adjusting noise suppression parameters based on background noise information collected by environmental sensors. For example, if a user is speaking on the street, the environmental sensors may detect high-frequency traffic noise. Based on this data, the system will enhance the suppression of this type of noise and focus the user's speech signal to ensure its clarity.

[0082] Next, adaptive filtering technology is used to adjust the filter parameters in real time during the denoising process. Adaptive filters can update their parameters in real time based on the characteristics of the input signal, allowing them to better adapt to constantly changing environmental noise. For example, in a noisy restaurant environment, this technology can automatically optimize processing to ensure effective differentiation between the user's voice signal and background noise, generating a clearer denoised speech signal. Through the combination of these technologies, the system can generate a high-quality preliminary denoised speech signal, laying the foundation for subsequent speech-to-text processing.

[0083] Finally, to further improve the denoising effect, the system can also employ multi-channel signal processing technology. This technology utilizes the phase difference processing of speech signals captured by multiple microphones to further reduce ambient noise. In specific implementations, the system processes the signal from each microphone synchronously, combining it with the previous dynamic noise suppression algorithm to generate an optimized denoised speech signal. This process ensures that the signal input to the speech recognition model is as clean as possible, significantly improving the accuracy of subsequent processing.

[0084] For the initial denoised speech signal, a speech recognition model based on an adaptive deep neural network is used. Combined with the semantic features in the contextual text information, speech-to-text processing is performed. Through context-aware technology, the recognition parameters are dynamically adjusted to adapt to the user's pronunciation habits and generate the initial text output.

[0085] In this stage, the system inputs the processed denoised speech signal into a speech recognition model built from an adaptive deep neural network (DNN). Unlike traditional models, this model can dynamically adapt to the user's pronunciation habits and the context. Specifically, the model quickly learns and adjusts its recognition strategy by comparing the user's real-time speech signal with previously collected speech data. For example, if the system detects that the user habitually speaks quickly, the model will adjust its parameters appropriately to adapt to this pronunciation pattern, ensuring accurate capture of the speech content.

[0086] By incorporating contextual text information, speech recognition models also consider previous user commands and the topic of the current conversation. Through context-aware technology, the model can analyze user intent in real time during the recognition process. For example, when a user says "I want to eat pizza," the model not only analyzes the speech but also reviews previous conversations to identify the relevant context of "eat," thereby enhancing its understanding and capture of the user's intent. Finally, after a series of processing steps, the model generates preliminary text output.

[0087] The key significance of this process lies in the fact that by dynamically adjusting recognition parameters, the system can adapt to different pronunciation styles and approaches of users, thereby ensuring the accuracy of text output. This adaptability is a crucial factor in enhancing the user experience, especially in complex and ever-changing natural language environments. In short, the high accuracy of voice input directly impacts a user's willingness to continue interacting with the system.

[0088] In this stage, the system inputs the denoised speech signal into a speech recognition model based on an adaptive deep neural network (DNN) for processing. First, the model extracts acoustic features from the input speech signal, using techniques such as convolutional neural networks (CNNs) to extract time-frequency features of the audio signal. These features include the frequency components of the speech and transient information of the time-domain waveform. The purpose of this process is to transform the original speech signal into a feature representation so that the deep learning model can perform subsequent processing.

[0089] Next, the system combines semantic features from the contextual text information to enhance the accuracy of the recognition results. By introducing context-aware technology, the model not only relies on the acoustic features of the audio input but can also refer to previous dialogue history or relevant information to better understand the user's intent. For example, when a user says "I want to play a song" through a voice assistant, the system will recognize the keywords "play" and "song" and combine them with contextual information to infer the user's specific needs and generate preliminary text output.

[0090] Finally, to adapt to different users' pronunciation habits, the system dynamically adjusts the recognition parameters. The adaptive deep neural network has the ability to learn and memorize the pronunciation patterns of specific users. The model optimizes the recognition algorithm by monitoring changes in the user's speech during input. For example, if a user is accustomed to rapid pronunciation, the system automatically increases the time window to capture each syllable in the rapid pronunciation, thereby improving the accuracy of text generation. This dynamic adjustment process ensures that the speech recognition model consistently provides high-quality output under different environments and contexts.

[0091] For the initial text output, a language model-based correction method is used, which combines contextual semantic features and grammatical rules to optimize the recognition results. Through dynamic threshold adjustment technology, the accuracy and consistency of the recognition results are ensured, and the final text output is generated.

[0092] In this step, the system inputs the initial text output into a language model for correction. The introduction of a language model allows the system to not only rely on the already generated text but also incorporate contextual semantic features and grammatical rules to further optimize the recognition results. Specifically, the system analyzes the initially generated text, compares it with known language patterns, and identifies potential spelling errors and grammatical inconsistencies. For example, if the user's instruction is "I want to find a restaurant," the system can anticipate the user's intent and correct it to "I want to go find a restaurant," improving the fluency and accuracy of the text.

[0093] Furthermore, the introduction of dynamic threshold adjustment technology allows the system to flexibly adjust its trust level in the recognition results based on context and user interaction feedback. For example, when the system detects that most of the content in the dialogue is related to "restaurant," it can appropriately reduce the tolerance for ambiguous words, ensuring that the final output text is not only grammatically rigorous but also aligns with the user's actual intent. During the correction process, the system monitors these parameters in real time and adjusts them rapidly based on previous user behavior to guarantee the accuracy and consistency of the recognition.

[0094] The significance of this process lies in the fact that, through language model correction, the system can generate text output that better aligns with user intent and natural language norms, thereby improving the effectiveness and fluency of subsequent processing. Intuitive feedback from the user experience is particularly important at this stage; only by ensuring high-quality recognition results can user satisfaction be improved and positive interaction between the user and the system be fostered.

[0095] When correcting text output, the system first evaluates the initial text results and then performs grammatical and semantic corrections based on existing language models. The construction of these language models relies on massive amounts of text data, enabling the identification of word combinations or grammatical structures that do not conform to linguistic conventions. For example, if the initial text output is "I want to go to the store to buy a bottle of water," but the user's actual intention is to go to the supermarket to buy water, the system will optimize it using contextual semantic features, ultimately correcting it to "I want to go to the supermarket to buy water."

[0096] Building upon this foundation, the system introduces dynamic threshold adjustment technology, dynamically adjusting the correction criteria based on the current context and user interaction feedback. By analyzing users' confirmation or negation behavior in text output, the model can learn and optimize the magnitude and direction of its changes. For example, if a user frequently confirms "go to the supermarket" instead of "go to the store," the system will record this habit and give it more priority in future processing to ensure that the output text better reflects the user's habits.

[0097] Finally, the corrected text output is compared again with the context information to ensure its accuracy and logical consistency. The system constructs a feedback loop, recording the confirmed text output as the user's new preference, forming a closed-loop learning mechanism. If the user uses similar expressions in subsequent conversations, the system will generate more accurate text output based on the previous correction results, resulting in a smoother interactive experience. Through this effective correction and optimization mechanism, the final generated text output will have higher accuracy, consistency, and user satisfaction.

[0098] S203, the text output is input into a semantic parsing model based on a graph neural network to perform contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop reasoning techniques to obtain the semantic representation of the user intent and its confidence score.

[0099] In practice, each word in the text output is first abstracted as a node in a graph structure, and the semantic relationships between these nodes are represented by edges. Through continuous updates to the dynamic graph structure, the model can flexibly respond to contextual information that changes over time, thereby enhancing its understanding of semantic relationships. Simultaneously, multi-hop inference technology enables the model to reason at multiple levels, uncovering implied meanings or potential user intentions. Therefore, the final model output will include a semantic representation of the user's intention and its confidence score, providing the necessary information foundation for subsequent operations such as response generation and task execution.

[0100] This step, by combining advanced graph neural network technology, enables the semantic parsing model to not only understand the surface meaning of text but also to delve deeper and uncover implicit relationships within it. Through in-depth analysis of the context, the model can accurately identify the user's intent, thus laying the foundation for providing personalized services and an efficient interactive experience. For example, if a voice assistant can accurately understand a user's needs—such as when a user says "I want to order food"—the system can not only recognize the intent to "order food" but also combine historical dialogue information to determine the user's preferences and possible choices, thereby providing services that better meet the user's expectations. This precise intent recognition capability is key to achieving more advanced natural language processing.

[0101] Specifically, for text output, a semantic parsing model based on graph neural networks can be used to abstract words in the text into nodes in a graph structure, and semantic relationships between words into edges. Through dynamic graph structure learning technology, a preliminary semantic graph structure can be generated.

[0102] In this stage, the system first converts each word in the text output into a node in a graph structure. This step includes word segmentation to ensure that the grammatical and semantic information of each word is preserved. Subsequently, semantic relationships between word nodes are established, using edges to represent connections between different words, such as synonyms, antonyms, or dependencies in the context. Through this graph structure, the system can flexibly represent information in the text, and through dynamic graph structure learning techniques, each node can interact with other nodes to obtain contextual information and implicit relationships. In this way, the model's reasoning ability is significantly enhanced, enabling it to capture more complex semantic structures.

[0103] This process, by abstracting text into a graph structure, provides a more intuitive and flexible framework for understanding and analysis. The introduction of graph neural networks enables the model to analyze semantics at different levels, more effectively handling long-distance dependencies and contextual information. For example, when user input contains multiple layers of meaning or implicit information, the system analyzes the graph structure layer by layer to extract the relationships between keywords, thereby more accurately understanding the user's intent. This capability significantly improves the depth and accuracy of semantic parsing, laying a solid foundation for subsequent user intent recognition.

[0104] In implementing this process, the system first performs word segmentation on the user's text input to extract keywords. Taking the sentence "I want to book a plane ticket for tomorrow" as an example, after word segmentation, the system uses words such as "I," "want," "book," "a," "tomorrow," "of," and "plane ticket" as nodes in the graph. Next, the system analyzes the relationships between these words, such as the relationship between the action of "booking" and "plane ticket," establishing edges to connect them. These edges can represent semantic relationships, such as the relationship between an action and an object. Simultaneously, if the context includes the time description "tomorrow," an appropriate time marker can be added to the graph.

[0105] After constructing the initial graph structure, the system employs dynamic graph structure learning techniques, allowing the representations between nodes to be dynamically updated based on contextual information. Specifically, as user input changes, the graph structure continuously adjusts to reflect new semantic relationships and contextual information. This is particularly important for capturing long-distance dependencies; for example, if the user subsequently mentions "night flight" or "business class," the system can promptly integrate these into the existing graph structure, forming a more complete semantic graph. The generation of this initial semantic graph structure lays the foundation for subsequent in-depth semantic analysis, enabling the model to better capture key intentions and details within the text.

[0106] For the preliminary semantic graph structure, a semantic analysis method based on multi-hop reasoning technology is adopted to capture the implicit semantic relationships and long-distance dependencies in the text. Through attention technology, the selection of reasoning paths is optimized to generate a preliminary semantic representation.

[0107] In this step, the system performs in-depth semantic analysis based on the initial semantic graph structure. Through multi-hop reasoning techniques, the model can perform multiple inferences within the graph structure, exploring implicit semantic relationships and long-distance dependencies layer by layer. For example, when analyzing sentences, the system not only relies on directly adjacent words but also delves into the slightly scattered key information within the text through the graph's hierarchical structure. Furthermore, to improve inference performance, the system incorporates an attention mechanism, focusing on the nodes most important for intent recognition, thus selecting inference paths more effectively. This dynamically optimized inference process allows the model to more comprehensively capture the text's inherent meaning and generate a preliminary semantic representation.

[0108] By applying multi-hop reasoning techniques, the model can gain a deeper understanding of the complex semantic structures in text, such as implicit meanings and long-range grammatical relationships. This capability significantly enhances the system's intelligence, enabling it to accurately grasp the user's true needs even when faced with ambiguous or polysemous user expressions. For example, when a user says, "I want to go to that special restaurant," the system might not accurately grasp the user's intention based solely on the surface words. However, through multi-hop reasoning analysis, the system can identify the meaning of "special" and connect it to the user's historical preferences, providing a more targeted basis for further responses.

[0109] In this step, the system performs an in-depth analysis of the generated preliminary semantic graph structure, employing multi-hop reasoning techniques to capture more complex implicit semantic relationships within the text. For example, in the context of a user expressing "I want to book a flight for tomorrow," the model not only focuses on the intuitive relationship between "book" and "flight" but also reasones multiple times to understand the impact of "tomorrow" on the overall behavior. Through multi-hop reasoning, the system can iteratively deduce from one node to another, gradually extracting more contextual information and potential user intent.

[0110] To improve the efficiency of the reasoning process, the system employs an attention mechanism to prioritize nodes that are more important in a specific context. Continuing with the example above, the system might identify the importance of the "ticket" node in the user's intent, and therefore allocate more computational resources during reasoning to nodes directly related to "tickets," such as "booking" and "travel dates." This targeted reasoning allows the system to more accurately capture the user's true intent in complex sentences and generate preliminary semantic representations.

[0111] The initial semantic representation obtained through multi-hop reasoning will contain the core information of the user's intent and its constituent elements. The system can output the corresponding structured representation, such as "the user wants to book a plane ticket tomorrow", and mark the key nodes involved and the relationships between them, thus providing a solid foundation for subsequent intent recognition and feedback mechanisms.

[0112] For the initial semantic representation, an intent recognition method based on a classification model is adopted. Combining contextual semantic features and user historical behavior data, the user intent is identified. Through confidence scoring technology, the accuracy and reliability of intent recognition are calculated, and a semantic representation of the user intent and its confidence score are generated.

[0113] The key to this process is inputting the initially generated semantic representation into a classification model to identify the user's intent. This model utilizes contextual semantic features and the user's historical behavioral data for training and prediction. First, the system converts the semantic representation into feature vectors suitable for the classification model to process. These feature vectors not only contain relevant information from the current dialogue but also incorporate the user's past interaction data to identify intents that better align with the user's preferences. Next, the model classifies the intent according to a predefined intent category (such as querying information, performing a task, or providing suggestions). Finally, the system calculates a confidence score for each identification result, reflecting the model's confidence in the outcome. For example, when a user says, "I want to book a flight for tomorrow," the system might analyze the data and identify the intent as "book a ticket," giving it a high confidence score, indicating that the model is very certain about this identification result.

[0114] The key to this step lies in clarifying the user's intent through a classification model, enabling the system to accurately respond to the user's needs. Confidence scoring provides a basis for further decision-making, helping the system assess the reliability of the identification results and avoid misjudgments. For example, when the user's expressed intent is ambiguous, the system can choose to ask for confirmation instead of directly executing an action. By combining contextual semantics and historical behavioral data, the model can not only improve the accuracy of identifying user intent but also provide a more personalized user experience. If the system can adapt to user preferences in a timely manner, it can effectively improve user satisfaction and loyalty.

[0115] At this stage, the system inputs the initially generated semantic representation into the intent recognition classification model. By combining contextual semantic features and the user's historical behavioral data, the system can construct a multi-dimensional feature vector. These feature vectors not only describe the intent of the current sentence but also incorporate the user's past behavioral patterns. For example, the system can analyze whether the user frequently books airline tickets in the past, their preferred departure and destination points, and their preferences for flight times, all of which are reflected in the semantic features through historical data.

[0116] During intent recognition, the classification model uses these feature vectors for training and prediction. For example, the model might recognize "I want to book a flight for tomorrow" as the intent "book a ticket," and simultaneously calculate a confidence score for this recognition, representing the model's confidence in this classification result. By using algorithms such as Softmax regression or neural networks, the system can normalize the probabilities of different intents, thereby assigning a confidence score to each intent.

[0117] Ultimately, based on the intent recognition results and their confidence scores, the system can generate a more accurate semantic representation of user intent. For example, if the model predicts a confidence score of 0.92 for "booking tickets," the system can confidently provide the user with relevant information about flight bookings; however, if the confidence score is below a certain preset threshold, the system can choose to use a confirmation question strategy, such as asking the user, "Are you trying to book a flight?" to ensure accurate response to the user's needs. This feedback mechanism based on accurate recognition not only improves the user experience but also enhances the system's intelligence level.

[0118] S204, based on the semantic representation and its confidence score, dynamically adjust the parameters of the speech recognition model and the semantic parsing model using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

[0119] The process begins by collecting user feedback data, including user responses such as confirmation, correction, or rejection of the speech recognition results. This feedback constitutes a valuable data source reflecting user satisfaction and accuracy with the system's recognition performance. Subsequently, the system combines real-time performance monitoring data to evaluate the model in real time, monitoring aspects such as recognition accuracy, response time, and overall user experience, thereby gaining a comprehensive understanding of the model's performance. Based on this data, the system generates optimization strategies and dynamically adjusts parameters to improve the model's performance under different conditions, ultimately achieving the goal of enhancing both speech recognition accuracy and semantic understanding accuracy.

[0120] The importance of this process lies in ensuring the system's self-improvement capabilities through continuous feedback loops, thereby enhancing the user experience. Direct user feedback provides the system with real-world data-driven insights, ensuring it adaptively adjusts to different voice inputs and environments, effectively reducing recognition errors and misunderstandings. The application of reinforcement learning in this process allows the system to improve its intelligence through continuous interaction, achieving fine-tuning and providing users with a more accurate and fluent voice recognition experience. This optimization mechanism not only enhances the system's intelligence but also lays the foundation for future development, enabling it to continuously learn and adapt to user needs in practical applications.

[0121] Specifically, based on semantic representation and its confidence score, a user interaction-based feedback data collection method can be adopted. Combined with the user's confirmation or correction behavior of the recognition results, user interaction feedback data can be generated. Lightweight data caching technology can be used to ensure the real-time and continuity of the feedback data.

[0122] This step involves collecting feedback data from user interactions to facilitate system self-optimization. The system monitors explicit user feedback on speech recognition results, determining whether the user accepts, corrects, or rejects these results. For example, when a user requests a setting from the voice assistant, if the assistant recognizes "turn up the volume" but the user corrects it to "turn down the volume," this correction information is valuable feedback data. Simultaneously, the system utilizes lightweight data caching technology to ensure the real-time and continuous nature of this feedback data during user interactions, preventing data latency from hindering the model's rapid learning and adjustment.

[0123] By capturing user feedback on system results in real time, the system can instantly identify shortcomings in accuracy and quickly reflect users' true needs. This process enables the system to have good adaptability and optimize for the usage habits of specific user groups. Through collecting a large amount of feedback data, the system's learning ability continuously improves, reducing errors in subsequent interactions and increasing user satisfaction and experience.

[0124] In this process, the system first builds a user interaction monitoring module, which tracks and records all interactions between the user and the speech recognition system. Whenever a user issues a voice command, the system captures the user's response. For example, a user might say, "Set the alarm for 7:00 AM." If the system misinterprets it as "Set the alarm for 8:00 AM," the user might immediately correct it, "Not 8:00, it's 7:00." Such confirmation or correction behaviors are valuable feedback information, which will be marked and saved.

[0125] To ensure the real-time nature and continuity of feedback data, the system employs lightweight data caching technology. When a user interacts, feedback information is temporarily stored in memory instead of being written directly to the database, thus avoiding response delays caused by data write latency. The system design incorporates message queue technology to push each user's feedback to the processing module instantly, preventing the loss of important interaction information. Furthermore, even if the user has not used the system for an extended period, previous feedback will be retained for subsequent analysis and optimization.

[0126] When generating user interaction feedback data, the system not only records users' corrective actions but also combines semantic representations and confidence scores to form a series of data features. For example, if a user corrects a specific command multiple times, the system marks this command as a high-error command and focuses on it during subsequent model training. Through these methods, the system can build a multi-dimensional user feedback data warehouse, providing a foundation for subsequent analysis and optimization.

[0127] The operation status of the speech recognition model and semantic parsing model is analyzed using a real-time performance monitoring method. Combined with recognition accuracy, response time and user satisfaction indicators, a performance monitoring report is generated. Dynamic threshold adjustment technology is used to ensure the accuracy and timeliness of the monitoring results.

[0128] In this step, the system monitors the overall performance of the speech recognition and semantic parsing models to ensure their efficient operation in practice. Through real-time performance monitoring, the system can continuously track multiple metrics of the model, including recognition accuracy, response time for processing requests, and user satisfaction. For example, if user feedback indicates a decline in recognition rate over a certain period, the system can immediately acquire and analyze the data. This data is then integrated to form a performance monitoring report, which helps the development team identify potential problems and formulate improvement measures.

[0129] The importance of real-time performance monitoring lies in ensuring the system is always operating at its best. The system can react quickly and adjust its strategies, preventing prolonged performance degradation that could lead to user churn. Dynamic threshold adjustment technology allows the system to flexibly adjust evaluation criteria based on different business needs and usage scenarios. For example, in a high-demand scenario, the system might adjust model parameters to improve recognition speed, while in situations requiring high recognition accuracy, it might favor comprehensive model optimization. This flexibility ensures the system consistently meets user needs and enhances the user experience.

[0130] To achieve real-time performance monitoring, the system needs to establish a multi-layered monitoring framework capable of tracking multiple performance metrics simultaneously. The system first sets key metrics, such as recognition accuracy, response time, and user satisfaction. For example, recognition accuracy can be calculated by comparing it with actual user input; response time is the time difference between the user issuing a command and the system providing a response; and user satisfaction can be measured through user feedback in subsequent conversations or through proactively given ratings.

[0131] To generate performance monitoring reports, the system periodically (e.g., hourly) summarizes and analyzes the monitoring data. The monitoring module inputs the collected data into the data analysis model to generate a report with detailed metrics. For example, if the system detects an increase in user dissatisfaction with the recognition results over a certain period, it will flag the data for that period and analyze potential causes, such as environmental noise interference or changes in the user's pronunciation.

[0132] When using dynamic threshold adjustment technology, the system can automatically adjust performance testing standards based on changes in real-time monitoring data. For example, when the system's recognition accuracy falls below a preset threshold, it can issue an alarm and quickly initiate a self-correction process to adjust the hyperparameters of the speech recognition model, ensuring that monitoring results remain highly accurate and timely even in abnormal situations. This technology not only improves model stability but also provides users with a smoother experience.

[0133] For user interaction feedback data and performance monitoring reports, an optimization strategy generation method based on reinforcement learning is adopted. By combining the parameter space of speech recognition and semantic parsing models, the model parameters are dynamically adjusted. Through a multi-objective optimization algorithm, the recognition accuracy and response time are balanced to generate a preliminary optimization strategy.

[0134] During this process, the system combines user interaction feedback data and performance monitoring reports, using reinforcement learning techniques to generate optimization strategies. The system treats metrics such as recognition accuracy and response time as states, and the corresponding adjustment strategies as actions. By analyzing historical interaction data and monitoring reports, the system can determine which parameter adjustments have led to positive improvements. For example, if a user group reports insufficient recognition rate at fast speech speeds, the system will use reinforcement learning algorithms to derive appropriate adjustment strategies to improve the model's adaptability to speech in such situations.

[0135] By combining reinforcement learning with feedback data, the system can continuously learn and improve, achieving self-optimization in different scenarios. This optimization strategy not only improves the accuracy of speech recognition but also optimizes response speed, making the system more flexible in handling multi-objective needs and truly meeting users' personalized requirements. The introduction of reinforcement learning adds momentum to the system's adaptive capabilities, enabling it to maintain good performance in dynamically changing environments.

[0136] In this step, the system comprehensively analyzes user interaction feedback and performance monitoring reports, and uses reinforcement learning techniques to generate optimization strategies. To implement this process, the system first needs to build a reinforcement learning framework that can model user feedback data to achieve learning and adaptation. By treating user feedback as reward signals, the system can use Q-learning or other reinforcement learning algorithms to progressively optimize the parameters of the speech recognition and semantic parsing models.

[0137] For example, when analyzing user feedback, the system found that recognition accuracy dropped significantly when users used voice commands in noisy environments. The system trained a reinforcement learning model, labeled this environment as a "low-recognition scenario," and applied an adaptive noise suppression algorithm in subsequent model adjustments. For instance, the system might adjust the weights of noise suppression to enhance recognition capabilities in this scenario.

[0138] Furthermore, to achieve multi-objective optimization, the system considers both recognition accuracy and response time during the optimization process. The algorithm continuously explores various parameter combinations and evaluates their impact on recognition performance, such as whether improving recognition accuracy leads to increased response time. Ultimately, the system generates an initial optimization strategy based on this feedback, ensuring efficient response capabilities while meeting user needs.

[0139] For the initial optimization strategy, a parameter adjustment method based on adaptive learning algorithm is adopted. Combined with real-time performance monitoring data, the parameters of speech recognition model and semantic parsing model are dynamically adjusted. Through feedback correction technology, the model performance is continuously optimized to generate the final optimization strategy.

[0140] In this step, the system further refines the initially generated optimization strategy. Through parameter adjustment methods based on adaptive learning algorithms, combined with real-time performance monitoring data, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted. The adaptive learning algorithm evaluates the current model's performance and user feedback based on real-time monitoring data, further adjusting the model's hyperparameters or network structure to adapt to constantly changing environments and user needs. For example, when user feedback indicates that the recognition rate is lower than expected under specific conditions, the system will utilize adaptive learning techniques to make necessary parameter corrections to the model to ensure better fulfillment of user expectations.

[0141] The importance of this process lies in its ability to allow the system to continuously reflect on and optimize its parameter settings during operation, ensuring that the model maintains optimal performance across various usage scenarios. By combining real-time performance monitoring, the system can flexibly respond to unexpected situations, such as adjusting the recognition algorithm under different environmental noise levels, thereby continuously improving the accuracy of speech recognition and the depth of semantic parsing. The application of this feedback correction technology gives the system a persistent self-improvement capability, providing users with a better interactive experience and enhancing the system's intelligence level.

[0142] During this step, the system will utilize adaptive learning algorithms (such as adaptive gradient algorithms and AdaBoost) to refine the initial optimization strategy. First, the system will analyze real-time performance monitoring data to identify the current model's performance in different scenarios, such as recognition effectiveness in noisy and quiet environments. This analysis will help the system determine the specific parameters that need adjustment.

[0143] For example, if user feedback indicates low recognition accuracy in a noisy coffee shop, the system can use an adaptive learning algorithm to prioritize adjusting noise suppression parameters to enhance speech signal clarity. In this case, the system can gradually adjust noise parameters and self-calibrate through online testing feedback to ensure optimal recognition performance in these specific environments.

[0144] Continuing to apply feedback correction technology, the system constantly collects user voice input and correction feedback. Each time a user provides feedback, the system immediately records it and incorporates it into the next round of model adjustments. For example, if a user continuously corrects a specific command, the system will update the model parameters based on these feedback records, thereby improving the recognition rate of that specific command. Ultimately, through this process, the system will generate a final optimization strategy, ensuring higher recognition accuracy and response speed across various usage scenarios, significantly improving the user experience. Through this continuous optimization process, the system evolves over long-term operation, adapting to the ever-changing needs of users.

[0145] As can be seen, multimodal data fusion is performed based on the user's voice signal, contextual text information, and environmental sensor data to obtain a fused multimodal feature vector. This multimodal feature vector is then input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing, resulting in text output. The text output is then input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition, yielding a semantic representation of the user's intent and its confidence score. Based on this semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using reinforcement learning-based feedback optimization techniques to generate a model optimization strategy. This approach improves the accuracy of speech recognition and the semantic understanding capabilities of natural language processing.

[0146] Another embodiment of the present invention provides an integrated system for speech recognition and natural language processing, see [link to relevant documentation]. Figure 3 The system may include:

[0147] The fusion module 301 is used to perform multimodal data fusion based on the user's voice signal, contextual text information and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector.

[0148] The processing module 302 is used to input the multimodal feature vector into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithm and context awareness technology to obtain text output.

[0149] The recognition module 303 is used to input the text output into a semantic parsing model based on a graph neural network to perform contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop inference techniques to obtain the semantic representation of the user intent and its confidence score.

[0150] The adjustment module 304 is used to dynamically adjust the parameters of the speech recognition model and the semantic parsing model based on the semantic representation and its confidence score using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

[0151] As can be seen, multimodal data fusion is performed based on the user's voice signal, contextual text information, and environmental sensor data to obtain a fused multimodal feature vector. This multimodal feature vector is then input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing, resulting in text output. The text output is then input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition, yielding a semantic representation of the user's intent and its confidence score. Based on this semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using reinforcement learning-based feedback optimization techniques to generate a model optimization strategy. This approach improves the accuracy of speech recognition and the semantic understanding capabilities of natural language processing.

[0152] This invention also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0153] Specifically, in this embodiment, the storage medium can be configured to store a computer program for performing the following steps:

[0154] S201, Multimodal data fusion is performed based on the user's voice signal, contextual text information and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector.

[0155] S202, the multimodal feature vector is input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithm and context awareness technology to obtain text output.

[0156] S203, the text output is input into a semantic parsing model based on a graph neural network to perform contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop reasoning techniques to obtain the semantic representation of the user intent and its confidence score.

[0157] S204, based on the semantic representation and its confidence score, dynamically adjust the parameters of the speech recognition model and the semantic parsing model using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

[0158] As can be seen, multimodal data fusion is performed based on the user's voice signal, contextual text information, and environmental sensor data to obtain a fused multimodal feature vector. This multimodal feature vector is then input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing, resulting in text output. The text output is then input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition, yielding a semantic representation of the user's intent and its confidence score. Based on this semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using reinforcement learning-based feedback optimization techniques to generate a model optimization strategy. This approach improves the accuracy of speech recognition and the semantic understanding capabilities of natural language processing.

[0159] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0160] Specifically, the aforementioned electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the aforementioned processor, and the input / output device is connected to the aforementioned processor.

[0161] Specifically, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0162] S201, Multimodal data fusion is performed based on the user's voice signal, contextual text information and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector.

[0163] S202, the multimodal feature vector is input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithm and context awareness technology to obtain text output.

[0164] S203, the text output is input into a semantic parsing model based on a graph neural network to perform contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop reasoning techniques to obtain the semantic representation of the user intent and its confidence score.

[0165] S204, based on the semantic representation and its confidence score, dynamically adjust the parameters of the speech recognition model and the semantic parsing model using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

[0166] As can be seen, multimodal data fusion is performed based on the user's voice signal, contextual text information, and environmental sensor data to obtain a fused multimodal feature vector. This multimodal feature vector is then input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing, resulting in text output. The text output is then input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition, yielding a semantic representation of the user's intent and its confidence score. Based on this semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using reinforcement learning-based feedback optimization techniques to generate a model optimization strategy. This approach improves the accuracy of speech recognition and the semantic understanding capabilities of natural language processing.

[0167] The above description, based on the embodiments shown in the figures, details the structure, features, and effects of the present invention. The above description is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the figures. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified to have equivalent changes, that do not exceed the spirit covered by the specification and figures, should be within the protection scope of the present invention.

Claims

1. A method for integrating speech recognition and natural language processing, characterized in that, The method includes: Multimodal data fusion is performed based on the user's voice signal, contextual text information, and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector. The multimodal feature vectors are input into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithms and context-aware technology to obtain text output. The text output is input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop inference techniques to obtain the semantic representation of the user intent and its confidence score. Based on the semantic representation and its confidence score, the parameters of the speech recognition model and the semantic parsing model are dynamically adjusted using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

2. The method according to claim 1, characterized in that, The process involves multimodal data fusion based on the user's voice signal, contextual text information, and environmental sensor data. This multimodal data fusion employs a deep learning-based multimodal alignment network, utilizing cross-modal attention techniques to extract the correlation between acoustic features and contextual semantic features in the voice signal, resulting in a fused multimodal feature vector, including: Based on the user's voice signal, contextual text information, and environmental sensor data, an edge computing-based data acquisition framework is used to acquire multimodal data in real time. Through an adaptive data cleaning algorithm, noise filtering and format unification are performed on the data to generate a preliminary standardized dataset. For the initial standardized dataset, a deep learning-based feature extraction method is used to extract acoustic features from the speech signal and semantic features from the contextual text information. Multi-head attention technology is then used to capture feature representations of different modalities and generate preliminary feature representations. For the initial feature representation, a feature fusion method based on cross-modal attention technology is adopted to weight and fuse acoustic features and semantic features. Through context-aware technology, the correlation between acoustic features and semantic features is captured to generate an initial fused feature representation. For the initial fusion feature representation, a feature integration method based on matrix factorization is adopted to map the fused features to a unified feature space. Through dynamic weight adjustment technology, the timeliness and consistency of the feature vector are ensured, and the final multimodal feature vector is generated.

3. The method according to claim 2, characterized in that, The step involves inputting the multimodal feature vector into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model, by combining dynamic noise suppression algorithms and context-aware technology, adjusts recognition parameters in real time to adapt to different environmental noise levels and user pronunciation habits, resulting in text output. This includes: For the speech signal in the multimodal feature vector, a preprocessing method based on dynamic noise suppression algorithm is adopted. Combined with the background noise information in the environmental sensor data, the noise suppression parameters are adjusted in real time, and an initial denoised speech signal is generated through adaptive filtering technology. For the initial denoised speech signal, a speech recognition model based on an adaptive deep neural network is used. Combined with the semantic features in the contextual text information, speech-to-text processing is performed. Through context-aware technology, the recognition parameters are dynamically adjusted to adapt to the user's pronunciation habits and generate the initial text output. For the initial text output, a language model-based correction method is used, which combines contextual semantic features and grammatical rules to optimize the recognition results. Through dynamic threshold adjustment technology, the accuracy and consistency of the recognition results are ensured, and the final text output is generated.

4. The method according to claim 3, characterized in that, The text output is input into a semantic parsing model based on a graph neural network for contextual semantic analysis and user intent recognition. The semantic parsing model uses dynamic graph structure learning and multi-hop inference techniques to capture implicit semantic relationships and long-distance dependencies in the text, obtaining a semantic representation of the user intent and its confidence score, including: For text output, a semantic parsing model based on graph neural networks is adopted, which abstracts the words in the text into nodes in the graph structure and the semantic relationships between words into edges. Through dynamic graph structure learning technology, a preliminary semantic graph structure is generated. For the preliminary semantic graph structure, a semantic analysis method based on multi-hop reasoning technology is adopted to capture the implicit semantic relationships and long-distance dependencies in the text. Through attention technology, the selection of reasoning paths is optimized to generate a preliminary semantic representation. For the initial semantic representation, an intent recognition method based on a classification model is adopted. Combining contextual semantic features and user historical behavior data, the user intent is identified. Through confidence scoring technology, the accuracy and reliability of intent recognition are calculated, and a semantic representation of the user intent and its confidence score are generated.

5. The method according to claim 4, characterized in that, The step involves dynamically adjusting the parameters of the speech recognition model and the semantic parsing model based on the semantic representation and its confidence score using a reinforcement learning-based feedback optimization technique. This feedback optimization technique, by combining user interaction feedback data and real-time performance monitoring, generates a model optimization strategy to continuously improve speech recognition accuracy and semantic understanding accuracy. This includes: Based on semantic representation and its confidence score, a user interaction-based feedback data collection method is adopted. Combined with the user's confirmation or correction behavior of the recognition results, user interaction feedback data is generated. Lightweight data caching technology is used to ensure the real-time and continuity of the feedback data. The operation status of the speech recognition model and semantic parsing model is analyzed using a real-time performance monitoring method. Combined with recognition accuracy, response time and user satisfaction indicators, a performance monitoring report is generated. Dynamic threshold adjustment technology is used to ensure the accuracy and timeliness of the monitoring results. For user interaction feedback data and performance monitoring reports, an optimization strategy generation method based on reinforcement learning is adopted. By combining the parameter space of speech recognition and semantic parsing models, the model parameters are dynamically adjusted. Through a multi-objective optimization algorithm, the recognition accuracy and response time are balanced to generate a preliminary optimization strategy. For the initial optimization strategy, a parameter adjustment method based on adaptive learning algorithm is adopted. Combined with real-time performance monitoring data, the parameters of speech recognition model and semantic parsing model are dynamically adjusted. Through feedback correction technology, the model performance is continuously optimized to generate the final optimization strategy.

6. A speech recognition and natural language processing integrated system, characterized in that, The system includes: The fusion module is used to perform multimodal data fusion based on the user's voice signal, contextual text information, and environmental sensor data. The multimodal data fusion adopts a deep learning-based multimodal alignment network and extracts the correlation between acoustic features and contextual semantic features in the voice signal through cross-modal attention technology to obtain the fused multimodal feature vector. The processing module is used to input the multimodal feature vector into a speech recognition model based on an adaptive deep neural network for speech-to-text processing. The speech recognition model adjusts the recognition parameters in real time to adapt to different environmental noise and user pronunciation habits by combining dynamic noise suppression algorithm and context awareness technology to obtain text output. The recognition module is used to input the text output into a semantic parsing model based on a graph neural network to perform contextual semantic analysis and user intent recognition. The semantic parsing model captures implicit semantic relationships and long-distance dependencies in the text through dynamic graph structure learning and multi-hop inference techniques to obtain the semantic representation of the user intent and its confidence score. The adjustment module is used to dynamically adjust the parameters of the speech recognition model and the semantic parsing model based on the semantic representation and its confidence score using a reinforcement learning-based feedback optimization technique. The feedback optimization technique generates a model optimization strategy by combining user interaction feedback data and real-time performance monitoring, thereby continuously improving the accuracy of speech recognition and semantic understanding.

7. The system according to claim 6, characterized in that, The fusion module is specifically used for: Based on the user's voice signal, contextual text information, and environmental sensor data, an edge computing-based data acquisition framework is used to acquire multimodal data in real time. Through an adaptive data cleaning algorithm, noise filtering and format unification are performed on the data to generate a preliminary standardized dataset. For the initial standardized dataset, a deep learning-based feature extraction method is used to extract acoustic features from the speech signal and semantic features from the contextual text information. Multi-head attention technology is then used to capture feature representations of different modalities and generate preliminary feature representations. For the initial feature representation, a feature fusion method based on cross-modal attention technology is adopted to weight and fuse acoustic features and semantic features. Through context-aware technology, the correlation between acoustic features and semantic features is captured to generate an initial fused feature representation. For the initial fusion feature representation, a feature integration method based on matrix factorization is adopted to map the fused features to a unified feature space. Through dynamic weight adjustment technology, the timeliness and consistency of the feature vector are ensured, and the final multimodal feature vector is generated.

8. The system according to claim 7, characterized in that, The processing module is specifically used for: For the speech signal in the multimodal feature vector, a preprocessing method based on dynamic noise suppression algorithm is adopted. Combined with the background noise information in the environmental sensor data, the noise suppression parameters are adjusted in real time, and an initial denoised speech signal is generated through adaptive filtering technology. For the initial denoised speech signal, a speech recognition model based on an adaptive deep neural network is used. Combined with the semantic features in the contextual text information, speech-to-text processing is performed. Through context-aware technology, the recognition parameters are dynamically adjusted to adapt to the user's pronunciation habits and generate the initial text output. For the initial text output, a language model-based correction method is used, which combines contextual semantic features and grammatical rules to optimize the recognition results. Through dynamic threshold adjustment technology, the accuracy and consistency of the recognition results are ensured, and the final text output is generated.

9. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1-5 when it is run.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-5.

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