1249 results about "Emotion recognition" patented technology

The invention discloses a Transform-based cross-modal fusion multi-modal emotion recognition method and device, which are used for solving the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in a multi-modal emotion recognition task, and the method takes the accuracy and robustness of emotion recognition as performance evaluation indexes. Firstly, feature information of three modes of vision, voice and text is obtained, feature extraction is performed on each mode through a deep learning model, then features of different modes are fused by using a cross-mode Transform module, and a complex dependency relationship between the modes is dynamically modeled through a multi-head self-attention mechanism, so that more accurate emotion recognition is realized, and the emotion recognition efficiency is improved. And finally, performing emotion prediction on the fused features based on time sequence modeling and an emotion classification module. According to the method, the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in multi-modal emotion recognition can be effectively solved.

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention discloses a robot anthropomorphic interaction method based on multi-modal emotion recognition and customized portrait generation. The method comprises the following steps: S1, dynamically fusing multi-modal emotions; the method comprises the following steps: S1, synchronously acquiring voice, visual and text signals through a multi-source heterogeneous sensor, capturing a user voice stream by a high-fidelity microphone array, and extracting acoustic characteristics such as intonation and speed, S2, performing cross-modal reasoning; s3, synchronously generating contents; step S4: style migration; step S5, anthropomorphic voice and expression generation; according to the method, man-machine interaction emotion is analyzed and generated by utilizing a large language model and multi-modal information fusion, the singleness of interaction emotion and the deficiency of emotional sharing ability are avoided, a strong emotion interaction characteristic is achieved, the image of the robot is obtained through a generative technology and can be migrated to any image, the limitation that a specific image is independently made is broken through, and the interaction effect of the robot is improved. The advantage that one robot can be suitable for different scenes is achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, cultural transmission and the like, and discloses a voice generation method based on multi-modal fusion, which comprises the following steps: collecting audio data to extract timbre features, and training a field feature timbre generation model; analyzing text semantic recognition emotion information, adjusting speech synthesis parameters, combining personalized information to construct a parameter mapping table, fusing to generate a synthesis control parameter sequence, aligning the synthesis control parameter sequence with character labels, visual elements and background music data, driving a domain feature timbre generation model, and generating synthesis data of synchronous speech, text, vision and music. Domain timbres are generated through timbre feature training, speech expression is optimized by combining semantic analysis and emotion recognition, user requirements are matched based on personalized information, and time alignment is performed by fusing text, vision and music data, so that the synthesized speech has domain features, emotion adaptability and personalization, and the speech quality is improved. And the voice immersion and the information transmission capability are improved.

Provided are a conversational artificial intelligence (AI) system and method based on real-time multimodal emotion recognition. The system includes a model server configured to provide a machine learning-based conversational model, a terminal configured to perform a conversation with the machine learning-based conversational model through the model server, display a virtual human responding to a user during a conversation with the user, and capture a facial image of the user during the conversation, and a multimodal empathetic conversation-generation system configured to access the model server and receive a response to a question of the user from the terminal, and assess an emotion of the user from the facial image of the user and control, based on the assessed emotion, an expression of the virtual human displayed on the terminal.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

The invention discloses a multi-mode interaction method for an accompanying robot, and relates to the technical field of robot interaction.The multi-mode interaction method comprises the steps that voice, visual and tactile signals are converted into quantum states through multi-mode quantum state coding, emotion weights are dynamically fused, and unified quantum representation is constructed; the dynamic quantum decision engine analyzes environmental noise and user emotion intensity in real time based on a quantum measurement theory, generates an adaptive strategy through dynamic modal weight distribution, and solves a multi-modal instruction conflict; holographic reinforcement learning optimization is combined with quantum acceleration calculation and classical reinforcement learning, the reward function weight is dynamically adjusted, cross-modal knowledge migration is achieved through the quantum tunneling effect, and the system strategy is continuously optimized. The problems that a traditional multi-mode interaction system is rigid in mode switching, high in emotion recognition error rate, lack of self-adaptive optimization of a feedback strategy and the like are solved, and the interaction efficiency and the user experience are improved.

The invention relates to the technical field of artificial intelligence and emotion calculation, in particular to an audio and video dual-mode emotion recognition method and system based on adapter fusion. The method comprises the following steps: acquiring a video frame sequence and an audio signal, and preprocessing the video frame sequence and the audio signal; constructing an emotion recognition model; based on a bimodal feature extraction module, a space adapter and a global adapter are embedded in sequence, and corresponding modal enhanced space features and global features are obtained in sequence; generating intermediate representations of the corresponding modes based on the global features, and performing feature fusion according to the intermediate representations to obtain fusion features of the corresponding modes; the fusion features are spliced, time sequence features are extracted, and final features are obtained; inputting the final features into a classifier to obtain a predicted emotion category, training an emotion recognition model by adopting a loss function, and determining an optimal emotion recognition model; and inputting a to-be-recognized video frame sequence and an audio signal into the emotion recognition model, and outputting a recognition result.

The invention provides a personalized English education system and method based on multi-modal sentiment analysis. The system comprises a multi-modal interaction module for receiving and processing multi-modal data of students, an emotion recognition module for performing emotion analysis on the input multi-modal data, and a personalized module for evaluating real-time learning states of the students based on historical learning data of the students, and the core brain module is used for dynamically adjusting interactive feedback according to the emotional state and the real-time learning state. The emotion recognition module comprises emotion information fusion, the emotion information fusion adopts a weighting strategy, and the final output emotion state is adjusted through emotion consistency constraint and a conflict correction mechanism. According to the invention, through an emotion consistency loss function, a conflict correction mechanism and a knowledge graph-based super-outline control mechanism, the emotion and cognitive states of the students are accurately identified.

The invention provides a dynamic self-adaptive multi-modal sentiment analysis fusion method and system, and relates to the technical field of multi-modal sentiment analysis. The method comprises the following steps: synchronously acquiring voice, text, facial expression and limb movement data of a target user to form a multi-modal data set; the method comprises the following steps: firstly, extracting emotional characteristics of each mode, and constructing a cross-mode correlation model to capture a collaborative and complementary relationship among different modes; and calculating a real-time confidence score and a complementarity index of each modal based on the weight matrix of the cross-modal correlation model. Then, according to the scores and the indexes, a weighted average or maximum entropy algorithm is dynamically selected to fuse multi-modal emotion features, and a comprehensive emotion feature vector is generated; and finally, inputting the vector into a pre-training deep learning model, and outputting an emotional state category of the user. According to the method, the user emotion can be accurately and comprehensively captured, efficient emotion recognition and classification are realized, and the robustness and flexibility of an emotion analysis system in a complex scene are improved.

The invention discloses a touch and talk pen intelligent children education method based on emotion recognition. The method comprises the steps that S1, facial expression data, voice and intonation data and touch behavior data of children are collected; s2, multi-modal high-quality emotion data matched with the actual emotion state is generated; s3, generating deep emotional features related to facial expressions, voices and tones and touch behaviors; s4, generating an emotional state label corresponding to the current emotional state of the child; s5, the generation model generates voice feedback content, image animation content and interactive game content which are adaptive to the current emotional state; s6, generating a personalized recommendation content list conforming to the emotional state of the child; and S7, displaying the education content in the personalized recommendation content list through a display screen, a voice output unit and a touch interaction unit of the touch and talk pen. According to the method, the fuzzy classification problem of the emotional state is solved in a touch and talk pen emotion recognition scene, and the adaptability of the model to the complex emotional state is remarkably improved.

The invention discloses a feature attention and bilinear gating fused speech emotion recognition method and device, and the method comprises the following steps: 1, collecting an audio file, obtaining corresponding label information, generating audio waveform and time frequency representation data through preprocessing, and constructing an audio waveform mask and a time frequency mask to mark an effective information region; 2, constructing a dual-path feature extraction module which comprises a time-frequency feature extraction module and a pre-training acoustic feature coding module; wherein the time-frequency feature extraction module models emotion correlation through local convolution and a multi-dimensional attention mechanism, and performs global time sequence modeling based on a bidirectional gated loop network; the pre-training acoustic feature coding module extracts high-level speech representation with high expression ability for emotion distinguishing by using a pre-training model; and step 3, constructing a feature fusion module and an emotion classification module, and combining with a dual-path feature extraction module to form a speech emotion recognition model.

The invention discloses a psychological accompanying method based on multi-modal emotion recognition, and belongs to the technical field of psychological health services, and the method comprises the steps: S1, synchronously collecting physiological signals, voice features, facial expressions and interactive behavior data of a user through a multi-modal sensor; s2, performing fusion analysis on the multi-modal data by using a deep learning model, and identifying a current emotional state and an emotional intensity level of the user; and S3, dynamically generating an adaptive psychological accompanying intervention scheme based on a preset emotion-intervention strategy mapping rule in combination with historical emotion data and personalized preferences of the user. According to the method, the physiological signals, the voice features, the facial expressions and the interactive behavior data are synchronously collected through the multi-modal sensor, the deep learning model is used for fusion analysis, and compared with single-modal recognition, the emotion state and the intensity level of the user can be judged more comprehensively and accurately, the emotion misjudgment risk is reduced, and a reliable basis is provided for subsequent intervention.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a context information-based voice emotion recognition method, device, equipment and medium, which comprises the following steps: receiving an original voice stream and generating an independent voice segment, recognizing a text and determining a speaker role type, and extracting an acoustic feature index; and generating a preliminary emotion label, generating context information in combination with the historical dialogue text, and inputting the context information, the preliminary emotion label, the speaker role type and the acoustic feature index into a multi-modal fusion module to generate an emotion judgment result. According to the method, multi-modal fusion is realized on the basis of context information by combining voice, text and role information, so that the emotion change of each role can be accurately recognized and understood in a complex dialogue scene, the problems of large single sentence emotion judgment error and neglect of the context information in a traditional method are avoided, and the accuracy and stability of emotion recognition are effectively improved.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

Methods and systems for generating training data for training a contrastive language-audio machine-learning model. A plurality of audio segments are retrieved from a speech emotion recognition (SER) database along with metadata associated with the audio segments. The metadata of each audio segment includes an emotion class. Words or terms associated with emotions are retrieved from a lexicon. A large language model (LLM) is executed on (i) the classes of emotion associated with the audio segments and (ii) the words or terms from the lexicon. This generates a plurality of text captions associated with emotion, which are stored in a caption pool. For each audio segment retrieved from the SER database, that audio segment is paired with one or more of the text captions from the caption pool that were generated based on the emotion class associated with that audio segment. This yields audio-text pairs for training a contrastive learning model.

The invention discloses a multi-mode electroencephalogram analysis model construction method and an online processing method and system. According to the method, multi-modal physiological signals such as electroencephalogram, electrocardio, skin electrical activity and eye movement are collected, and an integrated multi-modal signal collection device is used for preprocessing and feature extraction. A modal expert system based on an adaptive Transform architecture is adopted to replace a standard feed-forward network so as to enhance the feature processing capability. By means of the method, the features can be complemented under the condition of mode or data missing, the robustness, real-time performance and accuracy of the brain-computer interface technology in multi-mode data processing are improved, and the method is particularly suitable for the fields of emotion recognition, cognitive load monitoring, neural rehabilitation and the like.

The invention discloses a self-adaptive teaching strategy adjustment method based on sentiment analysis and a computer device. The method comprises the following steps: obtaining a target emotion feature data stream according to multi-modal student emotion data, and then extracting visual deep features, audio deep features and physiological deep features from the target emotion feature data stream; processing the visual attention weight, the audio attention weight, the physiological attention weight, the visual deep feature, the audio deep feature and the physiological deep feature according to a preset fusion strategy to obtain a multi-dimensional fusion feature, and analyzing the feature to obtain basic emotion recognition information of the individual student; analyzing the basic emotion recognition information through a built personalized emotion model of each student individual to obtain corresponding emotion state evaluation information; and on the basis of the constructed teaching knowledge graph and the emotional state evaluation information, generating a personalized teaching adjustment strategy so as to dynamically adjust an actual teaching scheme. According to the method, dynamic adjustment can be realized, and personalized teaching requirements are met.

The invention provides a digital human generation method based on a multi-modal large model. The method comprises the following steps: constructing a digital human basic model; generating a structured training set; generating a question and answer model supporting multi-channel interaction; semantic answers of the user questions are output, text emotional tendencies of the semantic answers are extracted, and emotional intensity parameters are output; generating facial muscle movement track data, and performing real-time rendering on the digital human basic model according to the facial muscle movement track data to output a digital human three-dimensional image with emotion expression. According to the embodiment of the invention, cross-modal alignment is carried out on text, image and audio data, and a multi-modal large model containing visual, voice and knowledge models is optimized by using a joint training method, so that more natural and smoother multi-channel interaction experience is realized; in addition, by introducing an emotion recognition model and a face interaction model, the emotion tendency contained in the semantic answer can be captured and reflected more accurately, so that a digital human three-dimensional image with real emotion expression is output.

At least one aspect of the present disclosure provides an emotion recognition method using an audio stream performed by an emotion recognition apparatus including receiving an audio signal having a preset unit length to generate the audio stream corresponding to the audio signal; converting the audio stream into a text stream corresponding to the audio stream; and inputting the audio stream and the converted text stream to a pre-trained emotion recognition model to output a multi-modal emotion corresponding to the audio signal.

The embodiment of the invention provides a digital human interaction system and method based on multi-modal emotion recognition, and belongs to the technical field of digital human interaction. The system comprises a multi-modal sensing module used for collecting multi-modal data and preprocessing the multi-modal data to generate a standardized data stream; the cross-modal fusion and emotion recognition module is used for carrying out interactive modeling on the multi-modal features and outputting a current emotion label and emotion intensity; the reaction planning module is used for generating a composite reaction strategy; and the digital human rendering module is used for mapping the composite reaction strategy into control signals corresponding to the voice, the facial expression and the action respectively, and driving a digital human to execute corresponding voice output, facial expression change and limb action through the control signals so as to realize interaction. According to the method, multi-modal data are deeply fused through the cross-modal graph neural network and comparative learning, the weight is dynamically adjusted in combination with the modal confidence, and the emotion recognition accuracy and robustness are improved.

The invention discloses an emotion recognition method and system based on cross-modal alignment and matching enhancement. According to the method, firstly, feature extraction is carried out on text, audio and video modalities in a data set, and then a text and audio cross-modal emotion alignment module and a text and video cross-modal emotion alignment module are constructed respectively, so that cross-modal semantic alignment is realized. Constructing an emotion label matching module based on an alignment result, generating modal pairs with similar emotions but different labels by using a difficult negative sample mining strategy, and paying attention to cross-modal emotion consistency through a dichotomy task guide model; performing modal feature fusion on the three modals through a six-layer attention crossing mechanism, finally splicing feature vectors, inputting the spliced feature vectors into a long-sequence context fusion modeling module for deep modal fusion, and capturing cross-modal interaction information; and the fused features are sent to an emotion classification module, and a final emotion category recognition result is output.

The invention discloses a multi-modal emotion fusion analysis method and system, and the method comprises the steps: carrying out the feature extraction of multi-modal emotion data modal by modal through a feature extraction module, and generating a text original feature, an audio original feature and a visual original feature; performing cross-modal alignment interactive fusion on the original text features, the original audio features and the original visual features based on a unified semantic alignment module, and constructing collaborative fusion features; performing mode and channel double-layer dynamic fusion optimization by adopting a dynamic fusion regulation and control module according to the text original feature, the audio original feature, the visual original feature and the collaborative fusion feature, and determining a unified fusion feature; performing hierarchical residual semantic gating enhancement based on the unified fusion features according to a high-order semantic abstraction module to generate semantic enhancement features; and inputting the semantic enhancement features into an emotion prediction module, and outputting an emotion analysis result. Based on the above scheme, a more stable, accurate and reliable emotion recognition result can be provided.

The invention relates to the technical field of natural language processing, in particular to a multi-modal dialogue emotion recognition method and system based on cross-modal fusion and comparative learning. According to the method, learnable residual scaling and pre-normalization are introduced through a cross-modal encoder, deep interaction of texts, voices and visual modals is stabilized, and gradient explosion is inhibited; a dialogue graph fusing semantic similarity and time proximity is constructed online through a semantic-time sequence graph enhancement module, and a graph attention network is used for explicitly modeling long-distance round dependence and cross-speaker emotion transmission; through an adaptive comparison and alignment module, a dynamic scheduling comparison loss and index moving average updated mode-emotion prototype library is adopted to realize data distribution adaptive cross-mode alignment; through cooperative work of the modules, the problems that in the prior art, cross-modal fusion is unstable, long-distance and cross-speaker dependence modeling is insufficient, and cross-dataset alignment capacity is weak are solved.

The embodiment of the invention provides an elderly emotion recognition method and device based on multi-modal perception, and the method and device achieve the optimization and enhancement of the signal quality through innovatively constructing a multi-modal data preprocessing mechanism and integrating the facial expression, voice and posture features. And designing a personalized feature mapping model based on historical emotion expression data, and establishing an adaptive feature fusion strategy for intelligent matching in combination with a cross-modal attention network. A hierarchical time sequence classification mechanism is introduced, dynamic modeling of the emotional development trend is realized through a long-short term memory network, and accurate prediction of the emotional state is supported. According to the method, the defects of the traditional technology in the aspects of multi-modal processing, personalized modeling, time sequence analysis and the like are effectively overcome, and the accuracy and reliability of sentiment recognition of the old people are remarkably improved.

The invention relates to the technical field of computers, in particular to a multi-modal model fusion-based water decision-making system, which comprises a virtual digital human interaction module, an intelligent agent business middle table and a large model technology base, and is characterized in that the virtual digital human interaction module is used for monitoring a voice instruction of a user; the method comprises the following steps: acquiring a voice instruction of a user, acquiring biological characteristic data of the user by using a multi-modal sensor, identifying relevance characteristics between the voice instruction and the biological characteristic data through a water conservancy emergency scene emotion identification model, acquiring indication information, and sending the indication information to an intelligent agent service middle station; the agent business middle platform performs task chain analysis on the indication information based on a water conservancy professional term intention recognition model to obtain different task requirements, and calls a large model technology base to make a decision based on the different task requirements to obtain decision information; a water conservancy large model trained based on a domain mechanism model library is integrated on the large model technology base. According to the invention, more efficient and intelligent services can be provided.

The invention relates to a multi-modal emotion recognition method based on heart and brain coupling and a graph neural network, and belongs to the field of artificial intelligence. Comprising the steps of data preprocessing, graph representation construction, multi-view graph convolutional network construction, fusion graph network construction and cross-domain joint optimization and sentiment classification. The method has the advantages that an adaptive adjacency matrix optimization strategy based on a triple constraint mechanism is proposed to solve the modal alignment and deviation problems represented by a multi-modal diagram in a data-driven branch, redundant noise is eliminated by adopting global regularization constraint, and unique feature representation in a modal is enhanced through modal specificity; a deep association rule is mined in combination with a cross-modal interaction module, the modeling ability of a heart and brain emotional state is improved, a multi-view image convolutional network is further designed, global features and local features are extracted, features of a cognitive heuristic branch and a data driven branch are combined by adopting an attention mechanism-based image fusion network, a domain confrontation strategy is introduced, and a cognitive network is constructed. And the generalization of the method is enhanced.

The invention discloses a multi-modal multi-label emotion recognition method and system based on modal contribution assessment, and the method comprises the steps: obtaining original feature sequences of a text mode, a visual mode and an audio mode, mapping the original feature sequences through an encoder, and obtaining high-dimensional feature embedding of the three modes; calculating a marginal contribution degree of each mode to emotion prediction at a sample level by utilizing emotion prediction values embedded by high-dimensional features of the three modes; according to the marginal contribution degree of each modal, fusing the features of each modal to form a fused feature, and processing the fused feature through a multi-modal encoder to obtain a multi-modal integrated feature; and through a trainable label embedding sequence, learning a dependency relationship between emotion labels, embedding and inputting the multi-modal integrated features and the labels into an emotion decoder to obtain a multi-modal emotion representation of mixed emotion, and inputting the multi-modal emotion representation into a multi-label classifier to obtain a predicted emotion label. According to the invention, the efficiency of multi-modal feature fusion and the accuracy of emotion recognition can be improved.

The invention relates to the technical field of emotion recognition, in particular to an odor-induced EEG emotion recognition method and system based on a space-time stratified attention mechanism, and the method comprises the steps: presenting odor stimulation to a testee through an olfactory stimulation device, and synchronously collecting an EEG signal and a free association voice text of the testee; performing semantic analysis on the free association voice text, and extracting keywords and corresponding emotion dimension tags; carrying out space-time hierarchical feature extraction on the EEG signal; constructing a space-time hierarchical attention mechanism model; and on the basis of the weighted spatial-temporal features and semantic tags, node connection weights of the individualized knowledge graph are updated through a graph neural network, and an emotion recognition result is generated. According to the method, spatial attention and time attention are combined, key spatio-temporal characteristics are dynamically focused, the limitation of a fixed weight of a traditional method is avoided, and the pertinence and integrity of neural signal representation are remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a multi-modal emotion recognition and interaction adjusting system and method based on uncertainty evaluation, and solves the defects of uncertainty processing, robustness of interaction strategies, complementary information mining degree among modals and the like in the man-machine interaction process in the prior art. Depth application finiteness is caused by lack of modeling for feature uncertainty after fusion. The uncertainty of an emotion recognition result is quantified through technologies such as multi-modal feature fusion and Bayesian neural network / model integration, an interaction strategy is dynamically adjusted according to an uncertainty score, emotion clarification or conservative response is triggered in a high-uncertainty scene, the robustness of human-computer interaction and the user experience are improved, and the user experience is improved. The method is suitable for intelligent customer service, government affair consultation, medical inquiry and other scenes with high requirements for emotion interaction accuracy.