616 results about "Language recognition" patented technology

The invention discloses a sign language translation method and device based on vision and word feature pre-training alignment, and belongs to the technical field of sign language translation.The sign language translation method comprises the steps that vision features of sign language videos are extracted through a vision encoder, and word text features are extracted through a text encoder; performing comparative learning based on vision and word text features to obtain a pre-trained vision encoder; performing word text mask prediction pre-training on the text decoder; constructing the pre-trained visual encoder and the pre-trained text decoder into a sign language recognition model so as to recognize a word text sequence from the sign language video; and accessing the pre-trained large language model in the domain into a sign language recognition model to construct a sign language translation model, and performing combined fine tuning so as to translate the word text sequence into a natural language text. The sign language recognition and translation method can realize more efficient, accurate and reliable sign language recognition and translation, is applied to the fields of intelligent sign language translation, barrier-free communication, sign language education and the like, and provides more accurate and natural language interaction experience for hearing-impaired groups.

The invention relates to a model for evaluating and predicting mild cognitive impairment risk of old people in a pension institution. The model sequentially comprises a behavior analysis module, a language recognition module, a social modeling module, a toughness calculation module, a feature fusion module, a risk reasoning module and the like. Behavior deviation characteristics and abnormal time periods are extracted by collecting behavior data of daily life, diet, social contact and the like of old people and comparing the behavior data with an institution work and rest template; in combination with nursing records, extracting language anomaly features; analyzing social frequency and structure changes in the abnormal time period, and extracting social variation features; a cognitive toughness index is calculated by integrating the health archive and the recovery ability to the health event; and performing toughness weighting on the multi-dimensional features to construct a time sequence tensor, and inputting the time sequence tensor into a recursive model to predict a cognitive impairment risk value. And if the risk value suddenly changes, the system automatically backtracks the feature trajectory of nearly 7 days, constructs and screens a prediction path with the strongest interpretation force, outputs a dominant prediction result and a key factor sequence, and realizes high-interpretability and high-reliability early recognition and intervention reference.

The invention provides an AI-assisted large-model intelligent question and answer accuracy improvement method, and belongs to the technical field of intelligent question and answer. Comprising the steps of data collection, data cleaning, data labeling, dynamic knowledge base construction, knowledge base updating trigger mechanism establishment, pre-training language model improvement, post-processing language understanding enhancement, combination of rule-based and machine learning reasoning, reasoning result verification and optimization, dialogue strategy optimization, emotion perception and response, multi-language search and language recognition search. And multi-dimensional evaluation index setting and regular evaluation and feedback improvement are realized. The intelligent question and answer accuracy improving method is high in understanding ability, timely in data updating, outstanding in reasoning ability and high in language recognition and dialogue ability.

According to the home assistant system based on the large language model agent, a voice interaction module receives voice input of a user and converts the voice input into a text, multi-language recognition and voice synthesis are achieved through natural language processing, and natural and smooth two-way voice interaction is supported. The WeChat interaction module receives and processes text or voice information sent by a user through WeChat, and remote instruction input and information feedback are achieved. The core of the system is a plurality of functional Agent modules. The task analysis Agent analyzes semantics input by a user based on a large language model and dispatches a corresponding functional Agent to execute tasks, and the functions of information query, smart home control, information transmission, mobile navigation, monitoring and the like are covered. Personalized learning and optimization can be carried out according to a user-defined information base, use habits and historical records, the task execution precision and efficiency are improved, and the method is suitable for multi-scene application in the field of smart home.

The invention discloses a continuous sign language recognition method based on multi-granularity cross-modal comparative learning. The method comprises the following steps: firstly, fully tapping a hidden semantic mapping relationship between a hidden sign language video and a corresponding sign language text; then, a multi-granularity cross-modal comparison mechanism is used for relieving potential influences of redundant frames and semantic irrelevant identification words on cross-modal alignment, and through a coarse-granularity video-sentence comparison strategy and a fine-granularity frame-word comparison strategy, visual and text modal features are effectively aligned from global and local angles; semantic interference caused by redundant frames and semantic irrelevant identification words is relieved through cross-granularity video-word comparison and cross-granularity frame-sentence comparison strategies; and finally, obtaining sign language text sentences through a decoder by utilizing visual embedding obtained in a visual encoder, and taking the sign language text sentences as predicted continuous sign language recognition results. The hidden semantic mapping relation between the sign language video and the corresponding sign language text is fully excavated, the semantic understanding of the sign language model on the visual context is enhanced, and the applicability and accuracy of sign language recognition are improved.

The invention discloses a judicial data file generation method and system and a computer program product. The method comprises the following steps: acquiring image data and audio data of an electronic file; preprocessing the electronic file image data; identifying the preprocessed electronic file image data by adopting an OCR (Optical Character Recognition) technology to obtain first text data; recognizing the audio data by adopting a language recognition technology to obtain second text data; fusing the first text data and the second text data to obtain fused data; analyzing the fusion data by using a pre-trained large language model, and extracting key information of the fusion data; and classifying the fusion data according to the key information, and allocating a corresponding label to each piece of fusion data to obtain a judicial data file in structured storage. According to the judicial data file generation method and system and the computer program product provided by the invention, the limitation of a conventional regular expression and NLP technology is overcome, and the accuracy and efficiency of judicial file structuring are improved.

The invention relates to the technical field of language recognition, in particular to a multi-dialect voice text conversion system and processing method, and the system comprises an audio collection module which is responsible for collecting voice input of a user and supports PCM, MP3 and WAV formats and sampling rates of 16kHz and 44.1 kHz; the dialect detection module is used for recognizing a dialect type of voice input by utilizing a pre-training language classifier; the voice feature extraction module is used for converting the voice signal into feature representation, such as Mel spectrum and MFCC (Mel Frequency Cepstrum Coefficient); the multi-dialect speech recognition module is used for mapping the features into a text sequence based on a speech recognition model of multi-task learning; and the text post-processing module is used for outputting a final standardized text according to a language rule and a context optimization recognition result. The multi-dialect support covers main dialect languages and can be quickly expanded to new dialects through transfer learning.

The invention discloses a sign language recognition system based on a dynamic time warping algorithm, and the system comprises a data collection module which is used for collecting gesture data in real time through a camera and a depth sensor; the feature extraction and time sequence conversion module is used for converting the gesture data into a time sequence; the dynamic time warping matching module is used for matching the input gesture with the gesture in the template library by utilizing a dynamic time warping algorithm; the matching result selection and template classification module is used for selecting the most matched template according to the similarity and determining the gesture category; the embedded platform and hardware acceleration module is used for executing a sign language recognition algorithm on the embedded platform and improving the recognition speed and real-time performance through hardware acceleration; and the character and voice output and real-time feedback optimization module is used for converting the recognition result into characters and voice output. Through hardware acceleration and data compression optimization, the sign language recognition precision, real-time performance and adaptability are improved, and the sign language recognition method is suitable for an embedded system with limited resources.

The invention discloses a skeleton sign language recognition method of a double-flow space-time dynamic graph convolutional network fused with residual learning, and belongs to the technical field of artificial intelligence and gesture recognition. According to the method, an input gesture skeleton sequence relative to a face is divided into two data streams, namely a hand form data stream and a wrist track data stream through double-reference-system differential homeomorphic mapping; the method comprises the following steps: firstly, processing hand posture data, and capturing a hand joint spatial topological relation by combining a spatial-temporal dynamic graph convolutional network (STDGCNN) with a residual convolutional block; meanwhile, a Finsler trajectory dynamics encoder (FTDE) is adopted to carry out differential geometric modeling on the wrist trajectory, and the direction sensitivity characteristic of the trajectory is captured through a multi-scale causal convolutional network. Then, mutual enhancement of double-flow features is realized through a bidirectional cross feature enhancer (BCFE), and the problem of geometric inconsistency of a heterogeneous feature space is solved through a geometric-driven optimal transmission fusion device (Geometric-OT). The method solves the challenge that a traditional sign language recognition method processes complex space-time correlation of gesture forms and motion tracks at the same time, the technical problems of insufficient relation between hands and faces, insufficient feature expression ability and low space-time feature extraction efficiency, and the problems of geometric inconsistency, single reference system and the like. And the identification accuracy and the real-time performance are obviously improved. Experiments show that the accuracy rate of the method in complex hundreds of sign language vocabulary recognition tasks reaches 95% or above on average, the reasoning speed is only 17ms on average, high-precision real-time sign language recognition is achieved, and the method has higher robustness in complex environments such as noise and shielding.

The invention discloses a sign language recognition method and device based on multi-modal deep learning, and the method comprises the steps: multi-modal data input: capturing hand motions, gesture tracks and facial expressions at the same time through a camera, a motion capture sensor and other devices, and forming multi-modal data input; sign language action recognition: precise recognition of sign language actions is realized through a combined model of a deep convolutional neural network and a long-short-term memory network; facial expression and gesture track combined recognition: realizing understanding and translation of complex sign language sentences by combining the captured facial expressions and gesture tracks; and context natural language processing: generating a target statement in combination with context semantic understanding, and outputting a translation result. Through hand motion capture, facial expression analysis, gesture trajectory tracking and context natural language processing, complex sign language motions can be recognized more accurately and translated into characters or voices in real time, and low delay and high accuracy are achieved.

The invention relates to an end-to-end mandarin and low-resource Cantonese unified recognition method, and belongs to the field of speech recognition. The method is applied to the scene of simultaneous recognition of mandarin and Cantonese. According to the method, an end-to-end CTC-Attention combined training method is adopted, a mandarin-Cantonese unified recognition dictionary is constructed, and the problem of confusion of context recognition in multi-language recognition is relieved in combination with a language recognition LID module. According to the invention, unified recognition of mandarin and low-resource Cantonese is realized by constructing an end-to-end speech recognition neural network model architecture, a modeling unit construction mode is provided, and the convergence performance of the model is improved. According to the end-to-end voice recognition method, the requirement of voice recognition can be met in the scene that mandarin and Cantonese appear at the same time, and good performance is achieved.

The invention belongs to a decoding method, and provides a multi-modal brain-computer interface decoding method and a related device for solving the technical problems that an existing non-intrusive brain language decoding method is insufficient in adaptability in global context and weak in generalization performance in a cross-subject scene, and multi-modal neural feature collaborative enhancement is difficult to achieve. And determining the called execution module. The execution module comprises a feature extraction module, a cross-subject standardization module, a multi-mode semantic fusion module, a language recognition module and a semantic consistency module. By obtaining the unified semantic representation and combining the beam search algorithm, the fairness and universality in different language groups are remarkably improved, multiple modes can be supported, and the brain signal decoding precision and robustness are improved. In addition, cross-subject semantic representation generalization can be realized, and individual specificity is effectively reduced.

The invention discloses an artificial intelligence automatic response conference assistant, and particularly relates to the technical field of intelligent video conferences. The system comprises a user interface used for displaying conference information, function options and execution results, a large language model used for natural language processing and understanding, and a language recognition module used for converting voice input into characters to achieve real-time conference recording and voice instruction recognition. By means of friendly user interface design, conference information, function options and execution results can be visually displayed, a user can easily operate and master the whole conference process, the voice recognition module adopts a high-quality recording module, voice content in a conference is accurately transferred into characters in real time, and the user experience is improved. The completeness and the accuracy of conference records are ensured, the large language model further performs semantic understanding and processing on the transferred text, intelligent conference abstracts and responses are generated, a user is helped to rapidly obtain key information, and the postprocessing module automatically sends the abstracts to a user mailbox to achieve the purpose of meeting. Or the system is automatically integrated to an online office system of a user, so that a lot of time and energy are saved.

The invention provides an information scheduling system and method based on a large language model. The system comprises a camera, a multi-mode large model, a scheduling terminal, a language recognition model, a cue word manager, the large language model, an API interface, a database, a POI library and an equipment information library. Compared with the prior art, the system has the natural language understanding ability, and a user can interact with the system through a daily language. Moreover, the system can carry out rapid analysis processing based on voice information, text information and road condition monitoring, and carries out cross-library joint query and information integration through an API interface on the basis of responding to a complex user intention, thereby obtaining a more accurate result in time.

The invention discloses an intelligent cockpit implementation method and system based on single-model multi-task reasoning, and belongs to the field of automotive electronics, and the method comprises the steps: inputting shared depth features into a detection head and a first classification head after original image data in an intelligent cockpit is preprocessed; the output of the detection head is subjected to detection post-processing to obtain a target detection anchor frame and position information, and the output of the first classification head is subjected to classification post-processing to obtain distraction identification classification; after the target detection anchor frame and the position information are preprocessed, human body area sub-images are input into a safety belt recognition and classification model; inputting the face region sub-images into a fatigue recognition classification model, a face ID matching model, a fixation point estimation model, a sight line estimation model and a head posture estimation model in parallel; and a face image sequence and a voice sequence in the continuous video stream are collected, the face image sequence and the voice sequence are respectively input into the lip language recognition model and the voice recognition model, a recognition result is input into the large language model to be processed to generate an interaction instruction, and a response is output through the voice synthesis module.

The invention discloses a voice interaction method, a server and a nonvolatile computer readable storage medium containing a computer program. The method comprises the steps of receiving voice request fragments forwarded by a vehicle in sequence; and performing natural language recognition on all the currently received voice request segments to obtain a first natural language recognition result. And obtaining a first natural language understanding result according to the first natural language recognition result. And performing natural language recognition on a received voice request to obtain a second natural language recognition result, the voice request being obtained by splicing all voice request fragments. And completing voice interaction according to the first natural language recognition result, the second natural language recognition result, the first natural language understanding result and the voice request. Thus, in the end-to-end architecture, through combination of the large language model and the streaming dialogue technology, the streaming natural language processing capability of the large language model is realized, the time required for waiting for the completion of the whole voice request is reduced, and the response time is shortened.

The invention discloses a neck-hung sign language interpretation device and a sign language semantic recognition interpretation method thereof.The neck-hung sign language interpretation device comprises a wearing support which defines a neck surrounding space and comprises a middle connecting arm and two shells, and the two shells are connected to the two sides of the middle connecting arm respectively; the image acquisition unit is arranged on the front side of the shell, comprises a three-dimensional depth camera and an adjusting mechanism for driving the three-dimensional depth camera to rotate, and is used for dynamically tracking and acquiring continuous sign language videos; the processing module is integrated in the shell, comprises a main control processor and a memory, and is used for operating a sign language recognition algorithm; the voice output unit is electrically connected with the main control processor and is used for playing the translation result; and the power supply unit supplies power to the image acquisition unit, the processing module and the voice output unit. Gesture languages used by the deaf-mute for communication are converted into natural languages which can be easily understood by normal people, the deaf-mute group is helped to speak, and the problem of information output when the deaf-mute communicates with the normal people is solved.

A sign language recognition system is described. The system includes a motion sensor, a processing circuitry and a display device. The motion sensor captures and records a dynamic sign language gesture as a sign video stream. The processing circuitry is configured with a key postures extractor, an accumulative video motion (AVM), and a sign recognition network (SRN). The key postures extractor captures main postures of the dynamic sign language gesture by extracting key frames in the sign video stream. The AVM captures motion of the sign video stream frames and transforms the motion in an AVM frame into a single AVM image. The SRN is configured as a convolutional network. The main postures and AVM image are fed into a two-stream network. The features from the two stream network are concatenated and fed into the SRN for learning fused features and performing classification of the sign language gesture.

The invention is suitable for the technical field of intelligent voice processing, and provides a language classification model training method, a language recognition method and device and intelligent equipment. The language classification model training method comprises the following steps: acquiring a first acoustic feature, wherein the first acoustic feature refers to an acoustic feature of an audio clip; the acoustic features of the audio clips are sequentially input into N layers of encoders of an initial language classification model, first encoding features output by the N layers of encoders respectively are obtained, and N is an integer larger than 1; inputting each first coding feature into a fusion module of the initial language classification model to obtain a first fusion feature; and based on the first fusion feature and a language recognition module of the initial language classification model, training the initial language classification model to obtain a target language classification model, the target language classification model being used for recognizing a language category of the audio data. According to the invention, the accuracy and robustness of language recognition can be improved.

The invention relates to the technical field of voice signal processing and synthesis, and particularly discloses an information processing method and system for voice conversion, and the method comprises the steps: extracting a language feature vector of each word from an input text; calculating posterior probabilities of the words in different languages by combining a Bayesian reasoning mechanism, and generating language attribution confidence coefficient characteristic values; a Monte Carlo sampling method is adopted to carry out multiple times of context sensitive simulation, and pronunciation path selection probability distribution characteristic values are generated; further fusing the feature values into a multi-language pronunciation decision vector, inputting the multi-language pronunciation decision vector into a multi-language end-to-end speech synthesis model, calling a phoneme mapping rule of a corresponding language and an acoustic parameter prediction module, and generating a high-quality target speech spectrogram; and finally, dynamically adjusting Bayesian prior distribution and a language recognition threshold according to the output speech spectrogram.

Disclosed in embodiments of the present description is a sign language recognition method, comprising: acquiring a video stream to be recognized, said video stream comprising continuous sign language action image sequences; acquiring a hand motion posture sequence corresponding to each sign language action image sequence in said video stream; inputting said video stream into a pre-trained first recognition model to obtain a first recognition result, the first recognition result comprising first probability distribution of each target word in a preset word list; inputting the hand motion posture sequence into a pre-trained second recognition model to obtain a second recognition result, the second recognition result comprising second probability distribution of each target word in the word list; and determining a target text on the basis of the first recognition result and the second recognition result. Correspondingly, disclosed in the present invention are a sign language recognition apparatus, and a corresponding interaction system and electronic device.

The invention relates to a sign language action recognition problem, and provides a sign language recognition method based on a millimeter wave radar 3D point cloud, sign language recognition is carried out in a sequence-to-sequence mode through a non-contact FMCW radar, and the sign language recognition method mainly comprises a first part, namely a continuous sign language action recognition method based on a millimeter wave radar; a second part: establishing an evaluation index STT Score of radar continuous sign language action recognition; according to the invention, a low-cost portable wearable acquisition device is adopted, so that a subject can use the device anytime and anywhere; meanwhile, millimeter-wave radar detection does not depend on light rays, the calculated amount is small, the privacy of a user is effectively protected due to the fact that signals collected by the millimeter-wave radar are radar radio frequency signals, and meanwhile the use feeling is good and the cost is low due to the non-contact collection mode of the millimeter-wave radar.

According to the method and device for generating the fine-grained semantic description from the action video data, the training data set is established based on the isolated word sign language recognition data set and the continuous sign language recognition data set containing the word target notation, and the action video data and the action description text data of fine-grained semantic description modeling are obtained; a fine-grained semantic action description style pre-training generation model is obtained through a training framework comprising an action video feature coding module, a multi-modal feature fusion module and a text feature coding module by combining user cue words and system cue words and introducing a mask reconstruction mechanism, action video data is adopted for fine tuning, a loss function is established, and a fine-grained semantic action description style is obtained. The fine-grained semantic action description generation model is used for generating high-quality fine-grained semantic action description data, and the problem that the current fine-grained semantic action description data is insufficient is solved. And the stability and the accuracy of a generated result are ensured when high-dynamic complex scenes such as sign language videos and interactive actions are processed.

The embodiment of the invention discloses a multi-language text transcription method and system, and is applied to an AR device end, the method comprises the following steps: collecting first to-be-transcribed data of a user through the AR device end, and preprocessing the first to-be-transcribed data to obtain preprocessed to-be-transcribed data, the first to-be-transcribed data comprising voice data, visual data and action data; performing feature extraction on the first to-be-transcribed data to obtain first to-be-transcribed feature data, the first to-be-transcribed feature data including first voice feature data, first visual feature data and first action feature data; inputting the first voice feature data into a preset language recognition model, outputting preliminary target language data, and sending the first to-be-transcribed feature data and the preliminary target language data to an edge device end; and receiving the text transcription result, and displaying the text transcription result on the AR equipment end, so that the problem of insufficient computing resources of the AR equipment end can be effectively solved, and the transcription accuracy and reaction speed of the AR equipment end are improved.

The invention discloses a sign language recognition glasses device, system and method, and belongs to the technical field of intelligent equipment, and the sign language recognition glasses device comprises a glasses frame which is used for supporting all components of the device; the camera is arranged on the front side of the glasses frame and is used for capturing hand actions in real time to generate image information; the data processing module is embedded in the glasses frame, electrically connected to the camera and used for processing the image information and executing sign language recognition; the main control unit is arranged on an ear rod of the glasses frame, is electrically connected to the data processing module and is used for coordinating the operation of each component; the display module is arranged at the positions of the lenses of the glasses frame, electrically connected to the main control unit and used for displaying character information obtained after sign language recognition, communication between hearing-impaired people and common people is not limited by specific places any more, and instantaneity and convenience of communication are greatly improved.

The invention relates to the technical field of video content processing, in particular to a real-time video sensitive content identification method and system, and the method comprises the following steps: S1, receiving a real-time video stream from a plurality of video sources, and splitting the real-time video stream according to frames; s2, performing language recognition on the audio data split in the S1, and generating corresponding text data; s3, identifying potential sensitive content, and labeling according to the type of the sensitive content; s4, updating the sensitive content definition library in real time according to the latest laws and regulations, social standards and user-defined rules so as to dynamically adjust the judgment standards of the sensitive contents; and S5, classifying the sensitive content preliminarily identified in the S3 according to the sensitive content definition library updated in real time in the S4. According to the method, the deep learning technology and the real-time updating mechanism are adopted, so that sensitive contents in the multi-source video stream can be effectively and quickly identified and processed, the accuracy of content monitoring is ensured, and the latest laws and regulations are met.

The invention discloses an artificial intelligence-assisted cross-language automatic note taking and term tagging system, and belongs to the technical field of language tagging. The system firstly obtains and processes audio data, generates a preliminary text through cross-language speech recognition, recognizes term candidate words in the text, and performs pronunciation association matching with a term knowledge base; and performing language recognition and context analysis on the candidate words and the contexts thereof, performing comprehensive pronunciation matching, language recognition and context score calculation to obtain a comprehensive score, thereby accurately selecting a cross-language term which most conforms to the current context as the final candidate word, and obtaining the annotation information of the final candidate word. And integrating the final candidate word and the annotation information of the final candidate word into the calibration note text, and outputting an automatic note with cross-language term annotations. According to the method, complex audios including language mixing and the like can be automatically processed, the high-quality calibration note text is generated, and the information recording efficiency and accuracy in a cross-language communication scene are remarkably improved.

The invention provides a system and method for digitally and intelligently recognizing a contract, and belongs to the technical field of character recognition, and the system comprises a data acquisition module which is used for obtaining an initial image of a target contract through image acquisition equipment, and preprocessing the initial image to obtain a standard image in an electronic copy format; the intelligent recognition module is used for recognizing the image text content of the standard image by using an OCR tool and converting the image text content into original text data; the text processing module is used for performing word processing on the original text data to obtain standard text data of the target contract; and the text analysis module is used for analyzing the standard text data by using a natural language recognition technology, extracting key contract data and integrating the key contract data into formatted contract data with a standard structure. Contract recognition efficiency and accuracy are improved, and a system recognition process is optimized.

The invention relates to the technical field of sign language intelligent communication, in particular to a deaf-mute sign language translation pronunciation system which comprises a data acquisition and preprocessing module, a sign language model recognition module, a dynamic gesture model module and a voice conversion module. According to the sign language translation pronunciation system for the deaf-mute, wide-angle camera intelligent glasses are adopted for visual collection, wearable equipment is not needed, video streams and thermodynamic diagrams are combined, visual information and key point probability distribution are complementary, the influence of single-mode noise is reduced, video space-time features are extracted through a 3D CNN, long-range dependence is captured in combination with a self-attention mechanism of Transform, and therefore the sign language translation pronunciation system for the deaf-mute is obtained. The sign language recognition precision is improved, an internal reference matrix and a distortion coefficient are calculated through a calibration board, wide-angle lens distortion is corrected, it is ensured that hand key points are accurately positioned, YOLOv8 is adopted to segment an interference object, background noise is prevented from affecting recognition, 21 key point thermodynamic diagrams are generated through MediaPipe, fault tolerance of low-confidence-coefficient key points is enhanced through Gaussian kernel diffusion, and the recognition accuracy is improved. And the model identification precision is improved.

The invention discloses a continuous sign language recognition method based on layered space-time enhancement. The method comprises the following steps: acquiring a sign language video; and inputting the sign language video into the trained sign language recognition model to obtain a first recognition result and a second recognition result, and taking the first recognition result as a final sign language recognition result. According to the continuous sign language recognition method based on hierarchical space-time enhancement, multi-stage output of a ResNet34 network is captured through an alignment module, and additional hierarchical alignment supervision is provided; according to the method, the alignment module and the timing causal module are integrated into the ResNet34 network, future and past information is aggregated through the timing causal module, so that more accurate vocabulary boundary perception is achieved, the alignment module and the timing causal module are integrated into the ResNet34 network, good balance between accuracy and calculation cost is achieved, an efficient and accurate solution is provided for a continuous sign language recognition task, and the continuous sign language recognition efficiency is improved. The recognition performance and robustness are remarkably improved, and the problem of under-fitting of the ResNet34 network in space and time contexts is solved.