655 results about "Recognition speech" patented technology

The invention provides an intelligent sound box voice processing method and system based on artificial intelligence, and the method comprises the steps: obtaining audio data and mouth shape video data, carrying out the processing of the audio data and the mouth shape video data, and carrying out the multi-modal feature fusion, and obtaining a fusion feature; performing bimodal voice activity detection on the fusion features to obtain effective voice data; performing context sensing recognition of audio and video fusion on the effective voice data to obtain a first text; constructing a user feature model, and performing semantic understanding on the text based on the model to obtain an understanding result; performing intention recognition and slot filling based on the understanding result to obtain user intention and key information; generating a response strategy in combination with the user intention, the key information and the environment perception data; generating response voice according to the response strategy; and monitoring feedback information of the user to the response voice in real time, and updating the user feature model and the response strategy evaluation model based on feedback. According to the scheme, the voice can be recognized more accurately, and the safety and robustness of the system are enhanced.

Automatic recognition of non-verbal messages in speech, and in particular to detection or analysis of prosodic multilayered analysis of intonation units such as prosodic unit prototypes and their multi-labeled variations, may form a hierarchical classification for the analysis of non¬ verbal information or cues in speech. A speech captured by a microphone is fed to a weakly- supervised deep learning acoustic model for speech recognition and transcription, that may be based on encoder-decoder Transformer architecture, such as Whisper by OpenAI. The model is trained to output multiple words form the text in the captured speech, to identify Intonation Units (IUs) that include one or more words, and associate non-verbal labels to each of the IUs. The labels may indicate a prototype, a discourse function (such as a conversation action), an emotion, an emphasis, or an attitude, as well as a genre of a part of, or whole of, the entire captured speech.

The invention discloses an intelligent oral English training system and method based on Prompt engineering, and relates to the technical field of oral English training. According to the method, a user is allowed to freely describe training requirements by using a natural language, the system automatically constructs a training Prompt, and personalized customization is realized; content deviating from a training target is accurately recognized through a deviation question detection module, a user is guided to return to a theme through real-time voice or text, and effectiveness and pertinence of training are guaranteed; a clear template definition ensures systematicness and reproducibility of a training task, and clear control and evaluation of a teaching target are facilitated; in combination with voice recognition, voice synthesis and text generation, natural interaction in a real context is realized, and the training experience is improved; on the basis of multi-dimensional automatic evaluation of a keyword hit rate, pronunciation quality and the like, personalized learning suggestions are supported; teachers are supported to generate training tasks in batches, students are supported to customize scenes, and different education environment requirements are met.

Disclosed are a multi-mode emotion recognition method, a system, an electronic device, and a storage medium. The method includes obtaining a spectrogram of a voice to be recognized and a corresponding text and inputting the spectrogram and the text into a multi-mode emotion recognition model to obtain an emotion recognition result output by the multi-mode emotion recognition model. The multi-mode emotion recognition model is trained based on a sample spectrogram, and a corresponding sample text, and a sample emotion recognition result, and is configured to extract a feature from the spectrogram and the text by a self-attention mechanism to obtain the voice features and the text feature, fuse the text feature and voice feature to obtain a multi-mode fusion feature, and make an emotion classification decision to obtain an emotion recognition result based on the text feature, the voice feature, and the multi-mode fusion feature.

The invention discloses a method for assisting in identifying telecommunication fraud through artificial intelligence, and relates to the technical field of artificial intelligence risk control, and the method comprises the steps: automatically identifying voice or text content through artificial intelligence, and extracting account and identity information; performing online verification on the account and the identity information, and under the condition that the identity cannot be verified, performing behavior monitoring and evaluating whether the user has a transfer intention or not; when the transfer intention exists, performing communication verification between the artificial intelligence and the cloud server, and if the communication fails or the verification is abnormal, terminating the transaction request; if the communication is normal and the user insists in payment, further judging whether the identity of the payee is credible or not, and if not, automatically transferring to the credible payee account to which the payee belongs; synchronously calling a block chain technology to record transaction information, generating a block chain smart contract, and locking funds to a contract account after user confirmation; and releasing the fund to the collection account after the smart contract condition is fulfilled, otherwise, returning the fund to the user account along the original path.

The invention provides an enterprise intelligent finance and taxation system fusing supervised learning and a block chain, and solves the core technical problems of insufficient data credibility, transparent contradiction between privacy protection and supervision, AI model training data island and the like of a traditional finance and taxation system. According to the system, an innovative technical fusion scheme is adopted, bank API, OCR recognition, voice input and other multi-source financial data are integrated through a multi-modal data fusion unit, and high-quality data fusion is achieved through confidence evaluation and an intelligent conflict resolution algorithm; an intelligent classification unit based on BERT deep learning integrates a pre-training model with a business rule engine and a gradient boosting decision tree, and accurate and automatic classification of financial transactions is achieved. A complete technical solution is provided for enterprise finance and taxation digital transformation, intelligent, automatic and credible processing of finance and taxation businesses is achieved on the premise that data safety and privacy protection are guaranteed, and the method has wide market application prospects.

The invention relates to the technical field of artificial intelligence, and discloses an emotion recognition method and device based on a voice large model, equipment and a medium, which are applied to a patient emotion recognition scene in the medical field, and the method comprises the steps: obtaining voice segment information and text description information corresponding to the voice segment information; performing feature coding and feature alignment on the voice fragment information and the text description information to generate joint features, and performing self-supervised training on the pre-trained voice large model through the joint features to generate an initial voice large model; collecting a voice instruction pair, and performing model fine tuning on the initial voice large model to obtain a fine-tuned voice large model; obtaining voice information with diversified emotion tags, optimizing the target function of the fine-tuned voice large model, and generating a target emotion recognition model; and obtaining to-be-recognized voice segment information, and performing emotion recognition based on the target emotion recognition model to obtain an emotion recognition result. The accuracy of emotion recognition is improved.

The invention provides a voiceprint recognition method and system, a terminal and a medium, and the method comprises the steps: carrying out the logarithmic Mel spectrum feature extraction of to-be-recognized voice data of a current speaker, further carrying out the embedded feature extraction based on a speaker classification model, generating an embedded feature vector, and carrying out the recognition of a voiceprint. Carrying out similarity calculation on the current speaker and template embedded feature vectors of one or more registered users, and verifying whether the current speaker is the registered user or not; the speaker classification model can adaptively adjust the attention to different frequency features by introducing a frequency self-attention mechanism, accurately identify key frequency information, and significantly improve the expressive force and accuracy of the model in feature learning; a multi-dimensional hybrid convolution strategy is introduced, so that the model has double capabilities of capturing frequency details and modeling time sequence dependence at the same time; and through a channel-by-channel separation convolution strategy, the calculation complexity and parameter quantity of the model are reduced, and the calculation cost is reduced.

The invention relates to a speech recognition and transcription method and system based on multi-modal fusion and sentiment analysis, and relates to the field of speech recognizing.The speech recognition and transcription method comprises the steps that a target speech signal and auxiliary modal information of synchronous visual information and text context information are obtained firstly, and the speech signal is segmented and recognized to obtain speech feature vectors; the method comprises the following steps: extracting text context information to obtain a text auxiliary feature vector, carrying out multi-modal fusion on the text auxiliary feature vector and the text auxiliary feature vector to generate fusion feature representation so as to carry out voice transcription to obtain an initial transcription text, and carrying out sentiment analysis according to visual information and the initial transcription text to generate a sentiment feature tag; and finally, optimizing and correcting the initial transliteration text based on the label to obtain a target transliteration text, thereby solving the technical problems that the speech recognition transliteration is difficult to adapt to dialect diversity and the recognition accuracy and robustness are insufficient due to neglect of emotion information, and improving the recognition accuracy and robustness through fusion of multi-modal information and emotion analysis. The voice content can be recognized more accurately, the transcription text can be optimized, and the accuracy and quality of voice recognition transcription are improved.

The invention belongs to the field of intelligent voice, and particularly relates to an intelligent health robot combining intelligent dialogue and health intervention, which comprises a voice recognition module and a rehabilitation guidance module, and is characterized in that the voice recognition module comprises a voice acquisition unit, a voice compensation unit and a multi-mode recognition unit; the method comprises the following steps: acquiring user voice by a voice acquisition unit, compensating formant features and pause discrimination by a discrimination filter, and obtaining a to-be-recognized voice feature space; correcting voice missing words and tones through a voice compensation unit to obtain a corrected to-be-recognized voice feature space; the multi-modal recognition unit outputs the voice text and the demand intention space according to the voice text and the demand intention space, and obtains an intention emotion state space by combining a preset evaluation interval; and finally, the rehabilitation guidance module calls an enhanced reasoning strategy library to generate a rehabilitation strategy intervention scheme according to the demand intention space and the emotional state space, and performs real-time voice output to realize intelligent dialogue and health intervention.

The embodiment of the invention discloses a voice recognition method and device, electronic equipment and a storage medium. The method comprises the steps that to-be-recognized voice is acquired; recognizing the voice to be recognized through the mixed voice recognition model to obtain a voice recognition result; the hybrid speech recognition model comprises a target coding module, a hybrid expert model MoE module and a target decoding module, the MoE module comprises a routing network and a plurality of expert network groups in one-to-one correspondence with multiple language types, and the routing network is used for carrying out target coding according to initial speech characterization; and determining a target expert network group corresponding to the initial voice representation from the plurality of expert network groups, and processing the initial voice representation through the target expert network group to obtain a target voice representation. According to the embodiment of the invention, the method improves the recognition accuracy of the mixed voice containing a plurality of languages.

The invention relates to the technical field of voice signal processing, and discloses a voice segmentation intelligent editing system based on deep learning. The system comprises a voice feature extraction module, a segmentation boundary detection module, a semantic content analysis module, an editing strategy generation module and a real-time quality evaluation module. The voice feature extraction module collects multi-dimensional voice features and timestamp information, and verifies feature integrity and timeliness; the segmentation boundary detection module identifies voice pause intervals and semantic turning nodes and divides segmentation units and boundary types; a semantic content analysis module extracts text content and emotion features of each segment, and analyzes semantic topic relevance and information density; an editing strategy generation module formulates a segmentation retention rule and a sequence adjustment scheme, and matches user preferences and scene demands; the real-time quality evaluation module monitors voice fluency and information integrity in the editing process and analyzes splicing errors and user feedback. According to the system, intelligent processing of the whole voice editing process is realized.

The invention discloses an intelligent voice interaction system and method based on multi-mode perception, and belongs to the technical field of man-machine interaction. The system comprises a voice acquisition and preprocessing module, a multi-mode perception module, a voice recognition, voice understanding and decision module and a multi-mode feedback module. Wherein the multi-mode sensing module fuses voice, vision and sensor data through a space-time alignment algorithm to realize comprehensive sensing of user intentions. According to the invention, through integration of information of a plurality of perception channels, the recognition accuracy and the understanding ability of the voice interaction system in a complex environment are improved, and the problem that traditional single voice interaction is difficult to recognize in a noise environment, a multi-person scene and a complex context is solved; and more natural and accurate man-machine interaction experience can be provided for the fields of intelligent home, intelligent vehicle-mounted, intelligent customer service and the like.

The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment, and medium for speech emotion recognition based on artificial intelligence. The method performs frame segmentation and windowing processing on speech information to be recognized to obtain a speech frame sequence, extracts a speech feature tensor and a text feature tensor of the speech information to be recognized, aligns the speech feature tensor and the text feature tensor and performs feature extraction to obtain multimodal features, performs average pooling processing and global maximum pooling processing on the speech frame sequence using a local window to obtain enhanced speech features, performs feature fusion on the enhanced speech features and the multimodal features, determines a fusion result, and obtains an emotion recognition result based on the fusion result. The method obtains low-level enhanced speech features through pooling processing, and performs feature fusion on the enhanced speech features and the multimodal features, thereby avoiding the degradation problem of deep networks, effectively improving the accuracy of speech emotion recognition while improving the generalization ability of the model.

The invention discloses an interactive question and answer task processing method based on an AI large model, and relates to the technical field of AI questions and answers, and the method comprises the following steps: performing correlation screening and function label labeling on high-confidence sub-queries in a retrieval result set, performing weight reduction on low-confidence sub-queries, constructing a cross-source consistency constraint vector, and generating an input data set; based on the input data set, a multi-source evidence consistency verification channel is constructed, entity-by-entity alignment and conflict detection are executed, the credibility interval of answers is calculated, meanwhile, voice emotions are recognized, and an answer data set is generated; and converting the answer data set into multi-modal feedback, monitoring user behaviors in real time, calculating behavior response strength indexes, dynamically adjusting a feedback form, and generating an interactive question and answer data set. The semantic consistency constraint of the multi-modal evidence is realized, and the robustness of answer credibility evaluation in the natural language processing task is improved.

The invention provides a voice recognition method, device and equipment and a computer readable storage medium. The method comprises the following steps: acquiring to-be-recognized voice, a trained voiceprint recognition model and a trained age prediction model; performing feature extraction on the to-be-recognized voice to obtain to-be-recognized voice features; performing prediction processing on the to-be-recognized voice features by using the trained voiceprint recognition model to obtain a first voiceprint vector corresponding to the to-be-recognized voice; performing prediction processing on the first voiceprint vector and the to-be-recognized voice feature by using a trained age prediction model to obtain an initial prediction result; and determining a target prediction result of the to-be-recognized voice based on the initial prediction result, and outputting the target prediction result. The voice recognition method and device can be applied to an anti-addiction system, and the accuracy of voice recognition can be improved through the voice recognition method and device.

Systems and methods for orchestrating interaction with an artificial intelligence (AI) application in a contact center environment receive, via an AI agent, a voice message from a user; convert the message from voice to text; generate an initial computational inference process based on the text message; determine whether or not all information required to execute the initial computational inference process is available to the processor; when a determination is made that all information required is available: execute the initial computational inference process; generate a text reply based on the initial computational inference process; convert the text reply to a voice reply; and send the voice reply to the user via the AI agent; when a determination is made that information is unavailable: generate a text query requesting the information; convert the text query to a voice query; and send the voice query to the user via the AI agent.

The invention provides a display device and a voice recognition method, and the method comprises the steps: obtaining an initial annotation text in a training corpus corresponding to voice data, and recognizing characters contained in the initial annotation text; determining whether the characters are replaced according to a preset probability; under the condition that replacement is needed, searching a replacement candidate list of the to-be-replaced character from the replacement table, and randomly selecting a target character from the replacement candidate list to replace the to-be-replaced character; generating a target annotation text according to the initial annotation text, the to-be-replaced character and the target character; and training a voice recognition model according to the target labeled text, and recognizing the to-be-recognized voice according to the trained voice recognition model. According to the method, character information with the same or similar pronunciation is introduced in the speech recognition model training process, so that the model can recognize characters in speech more accurately in the recognition process, the word error rate is effectively reduced under the condition of context bias, and the problem of low recognition accuracy in the current speech recognition process is solved.

The system provides a voice command recommendation to a user to avoid a non-voice command. The system determines a command that is expected to be received, and generates a voice command recommendation that corresponds to the predicted command. The predicted command can be based on the user's behavior, a plurality of users' behavior, environmental circumstances such as a phone call ring, or a combination thereof. The system may access one or more databases to determine the predicted command. The voice command recommendation may include a displayed notification that describes the recommended voice command, and exemplary voice inputs that are recognized. The system also activates an audio interface, such as a microphone, that is configured to receive a voice input. If the system receives a recognizable voice input at the audio interface that corresponds to the recommendation, the system performs the predicted command in response to receiving the voice input.

The invention relates to the technical field of voice recognition, and provides a voice recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the voice recognition of a to-be-recognized voice, and obtaining an initial voice recognition result; in the context of the to-be-recognized voice, based on the semantic information of the to-be-recognized voice, recognizing easily-confused segmented words existing in the initial voice recognition result; and performing word segmentation error correction on the initial speech recognition result based on pronunciation similarity between the easily confused segmented words and the context corresponding set segmented words to obtain a speech recognition result of the speech to be recognized. According to the speech recognition method and device, the electronic equipment and the storage medium provided by the invention, the easy-to-confusion words in the initial speech recognition result can be accurately recognized, so that the pronunciation similarity between the easy-to-confusion segmented words and the segmented words can be correspondingly set based on the context, the initial speech recognition result is accurately subjected to word segmentation error correction, and the speech recognition efficiency is improved. The voice recognition result of the to-be-recognized voice is obtained, and the voice recognition precision is improved.

The invention relates to the field of robot control, in particular to a mechanical arm control method and system based on voice control and multi-source sensing, and the method comprises the following steps: S1, recognizing a voice operation instruction, and generating a target object keyword and an action keyword; s2, performing analysis and target identification on the environment image to obtain depth information of each pixel point in the image and an identification frame of a target object corresponding to the target object keyword; s3, depth information of each pixel point in the recognition frame of the target object is extracted, and the position of the target object relative to the mechanical arm and the clamping posture of the mechanical arm are calculated; and S4, according to the position of the target object relative to the mechanical arm, the mechanical arm is controlled to move to the target position and clamp the target object in the clamping posture, and then the clamping force is calculated and adjusted according to the touch image obtained by the visual touch sensor. And S5, judging an action needing to be executed on the target object, and calling the custom performance function to implement a custom action on the target object.

The invention provides a voice recognition method and device, equipment, a storage medium and a program product, and relates to the technical field of image processing. The speech recognition method comprises the following steps: acquiring speech to be recognized, and extracting speech features based on the speech to be recognized; obtaining video content corresponding to the to-be-recognized voice, and extracting video features based on the video content; acquiring a historical voice recognition text of the to-be-recognized voice, and extracting historical text features based on the historical voice recognition text; obtaining a first multi-modal fusion feature based on the voice feature and the historical text feature; obtaining a second multi-modal fusion feature based on the video feature and the historical text feature; and generating a speech recognition text corresponding to the speech to be recognized based on the first multi-modal fusion feature and the second multi-modal fusion feature.

The invention relates to the technical field of data processing, in particular to a classroom teacher teaching performance description method, model and system based on multi-modal data fusion and a storage medium. By introducing a multi-modal data fusion strategy, cooperative processing of classroom teacher visual information, audio information and text information is realized, and the accuracy and time sequence continuity of teacher target perception are significantly improved. Compared with a traditional single-mode method, the method not only can extract the posture, expression and action characteristics of the teacher from the visual mode, but also can recognize the voice emotion and the side language signal from the audio mode, and achieves the full-dimensional description of the teaching behavior of the teacher in combination with the text semantics. The objective of the invention is to solve the problem of how to perform multi-dimensional evaluation on teaching performance of a classroom teacher based on data of multiple modalities.

The invention belongs to the field of voice recognition, and discloses a voice recognition method based on an acoustic model, computer equipment and a storage medium. The method comprises the following steps: acquiring voice features of to-be-recognized voice; inputting the voice features into an acoustic model, and outputting a recognition result by the model; wherein the time sequence processing network layer firstly determines the ratio of current input future frames needing to be pre-watched to context information through a pre-trained gating fusion unit, then calculates the number of the future frames needing to be pre-watched based on the ratio, obtains the corresponding future frames, calculates long-time context representation in combination with the future frames, processes the long-time context representation and outputs the long-time context representation to the next layer of network. According to the method and the device, the problem of static binding of delay and accuracy in the prior art is solved by dynamically adjusting the number of the future frames to be pre-watched, low-delay response to simple command words is realized, the recognition accuracy is improved through multiple future frames to be pre-watched for easily-confused instructions, the balance of the delay and the accuracy is realized, and the performance of a voice recognition system and the user experience are improved.

According to the intelligent voice broadcast system and method for the elevator, elevator car environment data are collected in real time through a multi-source heterogeneous sensor set, and passenger density distribution and movement tracks are calculated through fusion of a millimeter wave radar and a 3D visual sensor; physiological features of a handrail contact area are detected through a biological impedance sensor so as to judge the health state of a passenger, and the current signal-to-noise ratio is dynamically calculated and the voice frequency band is optimized through an environment noise analysis module. Therefore, by constructing a technical system of'multi-modal perception-edge intelligent decision-cross-modal interaction 'and adopting innovative means such as millimeter-wave radar and biological impedance fusion sensing, deep reinforcement learning of a dynamic strategy and a phased array directional sound field, the problems of broadcast stiffness, poor scene adaptability and privacy potential safety hazards are solved. According to the system and method, accurate passenger feature recognition, dynamic voice content generation and multi-channel collaborative feedback are achieved through two-dimensional innovation, and the intelligent level and service efficiency of elevator voice interaction are improved.

The invention relates to the technical field of voice control of household electrical appliances, particularly provides a voice control method of an intelligent household electrical appliance and the intelligent household electrical appliance, and aims to solve the problem that an existing voice control system cannot accurately recognize an effective voice instruction in a noisy environment. In order to achieve the purpose, the voice control method of the intelligent household electrical appliance comprises the following steps: after a voice control function is started, collecting audio data within a preset duration; environment noise and voice instructions of the surrounding environment in the audio data are recognized; when the environmental noise is greater than or equal to a preset noise threshold value, acquiring a voice keyword in the voice instruction; according to the voice control method disclosed by the invention, when the surrounding environment noise is greater than the preset noise threshold value, the environment noise and the voice instruction in the acquired audio data can be identified, and the intelligent household electrical appliance is controlled by identifying the keyword in the voice instruction; the accuracy of recognizing the voice instruction in the audio data is improved.

The invention provides a voiceprint recognition method and device, electronic equipment and a storage medium, and belongs to the technical field of voice recognition, and the method comprises the steps: training a generative adversarial network based on a training data set; iteratively updating the generation sample proportion of a generator in the generative adversarial network based on the performance of a discriminator in the generative adversarial network on the verification data set to obtain an optimal generation sample proportion; training a voiceprint recognition model based on the optimal generated sample proportion to obtain a target voiceprint recognition model; and inputting the voice to be recognized into the target voiceprint recognition model to obtain a voiceprint recognition result output by the target voiceprint recognition model. According to the method, the generative adversarial network is applied to generate and synthesize the voice sample, so that the problems of limited data set and insufficient diversity in voiceprint recognition are solved; in the model training process, the number of the generated samples is gradually adjusted, the optimal proportion of the generated samples to the real samples is found, it is ensured that the model keeps high precision, and the good generalization ability is achieved.

The invention discloses a voice conversion authentic identification method and system based on knowledge distillation alignment, and is applied to the technical field of voice authentic identification. The method comprises the following steps that a double-branch model used for voice authentic identification is constructed, pure voice serves as input of a teacher branch, noisy voice serves as input of a student branch, and the teacher branch and the student branch share a feature extraction network of the same structure; applying a speech enhancement technology at the front end of a student branch to generate an enhanced speech signal; deep features are extracted, and alignment of the deep features in a hidden space is restrained through a knowledge distillation loss function; carrying out dynamic weight fusion on the aligned deep features through a fusion weight; and training a classifier, performing joint optimization in combination with classification loss and knowledge distillation loss, and outputting a voice authentic identification result. According to the method, distribution alignment of pure and noise features is realized through knowledge distillation, forged traces in voice conversion are effectively recognized, and high detection precision is still kept in complex noise and unknown attack scenes.

The present invention relates to a technology capable of identifying a companion animal by analyzing video, image, and voice data collected through CCTV or a camera, for management of companion animals and tracking in case of loss thereof, wherein, by simultaneously or sequentially using at least one identification method among facial recognition, nose print recognition, voice recognition, and motion recognition by analyzing a video, image, or voice, an effect of greatly improving the reliability of object identification for companion animals can be provided.

The invention discloses a voice recognition method and device and electronic equipment. The method comprises the following steps: acquiring a voice to be recognized, the voice to be recognized being a voice in a dialect form; performing voice recognition on the to-be-recognized voice through the voice recognition model to obtain an initial recognition result of the to-be-recognized voice, the initial recognition result including a word sequence of the to-be-recognized voice and a confidence coefficient corresponding to the word sequence; under the condition that the confidence coefficient is smaller than a confidence coefficient threshold value, a plurality of candidate vectors with the similarity with the word sequence in a preset number are determined in the professional domain knowledge base and the dialect knowledge base respectively; and determining a target prompt word at least according to the to-be-recognized voice, the confidence coefficient and the plurality of candidate vectors, and correcting the word sequence according to the target prompt word through a voice correction model to obtain a target recognition result. According to the invention, a technical problem of low speech recognition accuracy in a scene of combining a professional field and a dialect in a speech recognition method in the related technology is solved.