48 results about "Emotional score" patented technology

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention discloses a VEM-Token vocal music emotion multi-mode token song sound and accompaniment deep learning method, which is different from an existing method that information is segmented into textual token lexical elements by artificial intelligence and then recognition is carried out on the textual token lexical elements. The method comprises the following steps: performing frequency spectrum processing on a vocal music file, detecting rhythm, segmenting the vocal music file subjected to frequency spectrum processing into a VEM-Token sequence according to the vocal music rhythm, establishing a VEM coordinate system, a VEM function and a VEM library according to multiple modes such as lyrics, singing sound, accompaniment, singer emotion, accompaniment emotion, video and image, performing VEM-Token identification, separating a singing sound stream and an accompaniment stream, and determining the vocal music according to a vocal music expert. And performing multi-modal emotion scoring on the vocal music sample, obtaining a VEM parameter by adopting supervised learning and deep learning algorithms, and learning to obtain the multi-modal emotion of the vocal music sample. For other vocal music works, vocal music multi-mode emotions can be recognized, and a lyric score, a VEM-Token song sound score, a VEM-Token accompaniment score and a VEM-Token music score are output. AI systems including a common large model and the like are accessed, and a vocal music agent Agent capable of listening to the singing and identifying the music score is developed.

A system for analyzing audio includes a memory configured to store known digital audio representation containing known fraudulent audio streams and a processor operably coupled to the memory. The processor receives a portion of an audio stream from an external device and produces a transcript of the portion of the audio stream. The processor then determines a timing score, an emotional score, a background score, and a content score by analyzing the portion of an audio stream and the corresponding transcript and comparing them to the known digital audio representations and transcripts. The processor then determines if the audio stream is malicious by combining the timing score, emotional score, background score, and content score to produce a combined score and comparing the combined score to a threshold. The processor notifies a user that the call may be fraudulent when the combined score is greater than the threshold.

The invention discloses a forum lever speech recognition method and device based on deep learning. The method comprises the steps of obtaining comment data and performing preprocessing; calculating a text sentiment score based on sentiment words, negative words, adverbs and exclamation marks in the preprocessed comment data; calculating emoticon emotion scores based on emoticons in the comment data; obtaining a final emotion score based on the text emotion score and the expression emotion score; vectorizing and compressing the preprocessed comment data to obtain text sentence vector representation; performing weighted fusion on the text sentence vector representation and the final sentiment score to obtain sentiment value weighted sentence embedding representation; using the sentiment value weighted sentence embedding representation and the corresponding label to train a sentiment classification model; the trained sentiment classification model is used for identifying whether the speech is a bar-sperm speech or not. According to the method, sentiment words, negative words, adverbs, other words, exclamation marks and emoticons are comprehensively considered, and the recognition problem of the lever speech containing hidden contexts is solved.

The invention relates to the technical field of data analysis, and provides a user emotion recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: generating a target emotion change curve according to a pre-obtained historical video call record of a target person, carrying out the trend analysis of the target emotion change curve, and obtaining an emotion change habit; acquiring real-time call information of the target person, and performing dynamic weight division on the real-time call information based on an emotion change habit to obtain an output weight; performing standardization processing on the real-time call information, and performing weighted fusion on the processed real-time call information according to the output weight to obtain a real-time emotion score; and obtaining a current emotion type of the target person according to the real-time emotion score, generating a prompt interface according to the current emotion type, and displaying the prompt interface on a terminal interface. According to the method, the emotion recognition precision, robustness and practical value are remarkably improved, the method can be widely applied to scenes such as video calls and intelligent customer service, and the man-machine interaction experience is improved.

The invention discloses a legal consultation risk scoring method and system fusing emotional intensity and social influence, and belongs to the technical field of artificial intelligence, and the method comprises the steps: constructing an emotional vocabulary library intensity table, and calculating an emotional score of a consultation text in combination with the emotional vocabulary library intensity table; and constructing a social influence strength table, calculating a social influence score of the consultation text in combination with the social influence strength table, and obtaining a risk score of the consultation text based on the emotion score and the social influence score. High-risk consultation can be recognized in an early stage, processing resources are reasonably distributed, potential legal crisis and social events are prevented, meanwhile, the transparency requirement is met, and intelligence of legal services and modernization of social governance are promoted.

The invention discloses a campus bullying early warning method and system based on artificial intelligence, and relates to the technical field of campus psychological health monitoring, and the method comprises the following steps: obtaining a drawing emotion score of a student; the drawing emotion score is obtained by collecting and processing drawing data of a student in a drawing scene; whether the drawing emotion score is larger than a preset threshold value or not is judged, if yes, an interactive dialogue of the drawing scene is generated, and dialogue texts, voice fundamental frequency data of students, eye movement track data and statement complexity data in the interactive dialogue process are collected in real time. According to the scheme, the local emotion region is positioned through semantic segmentation, and the emotion weights of different regions are quantized in combination with the convolutional neural network, so that directional questioning can be performed for specific emotion sources in the question generation process, more real emotion feedback is triggered in subsequent data acquisition of voice fundamental frequency, eye movement tracks and the like, and the accuracy of question generation is improved. And a high-quality multi-modal analysis data basis is provided for campus bullying early warning.

The invention discloses a teaching quality analysis method, system and device based on big data and a medium, and relates to the field of teaching quality evaluation. The method comprises the following steps: adjusting initial difficulty according to the correlation between the content of teaching courseware and a preset key point to obtain first difficulty; obtaining first comprehensive emotion values of all students in each first preset time period from the teaching video, integrating the plurality of first comprehensive emotion values to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value; determining a score increase value according to the first score set and the second score set, determining the weight of each student according to the classroom participation degree of each student in a second preset time period, and obtaining a score score according to the weight and the score increase value of each student; and determining the teaching quality in the second preset time period according to the plurality of emotion scores and score scores in the second preset time period. By implementing the technical scheme provided by the invention, the teaching quality can be comprehensively and accurately evaluated.

The invention relates to the technical field of emotion supervision, in particular to a middle school student emotion supervision system based on AI assistance, which comprises a voice acquisition module, a voice text extraction module and an emotion scoring module, then, a voice text extraction module constructs a deep neural network model, voice texts corresponding to the digital signals are extracted through the deep neural network model, emotion vocabularies in the voice texts are extracted again through an emotion scoring module, and different emotion vocabulary sets and corresponding scores are formulated; and the emotion vocabularies in the emotion vocabulary set are associated with the digital signals in the voice acquisition module, so that when the voice text extraction module and the emotion scoring module extract the voice files and the emotion vocabularies, the error and uncertainty when the emotion vocabularies and the voice texts are extracted are reduced.

A method for generating predictions related to a set of input text is provided. Text may be received. A machine learning model may be utilized to determine a set of classification probabilities of the text relative to a set of anchors. An emotional score indicative of emotional content of the text may be determined based at least in part on a convex combination of the set of probabilities for the text. One or more predictions related to the text may be generated.

The invention relates to the technical field of smart home, and discloses an illuminating lamp control method and related device for predicting user emotion based on voice, and the method comprises the steps: collecting a voice signal of a to-be-recognized user through a voice collection module after an illuminating lamp is turned on and enters an intelligent mode, collecting environment data, and obtaining the attribute information of the to-be-recognized user; and inputting the voice signal, the environment data and the attribute information into a user emotion recognition model to obtain an emotion score of the to-be-recognized user, determining a target color temperature and target brightness of the illuminating lamp based on the emotion score, adjusting the color temperature of the illuminating lamp to the target color temperature, and adjusting the brightness of the illuminating lamp to the target brightness. According to the invention, the emotion of the user is recognized according to the speech of the user, the attribute information and the environment where the user is located to control the color temperature and brightness of the illuminating lamp, the illuminating lamp can be adjusted to the light color temperature and brightness matched with the emotion of the user without user intervention, and the intelligent degree of illuminating lamp control is improved.

The present invention relates to a multi-modal emotion recognition method and system for companion robots. The method includes separately collecting facial expression pictures, voice signals, and electroencephalogram signals; extracting emotional feature vectors of facial expressions, emotional feature vectors of voices, and feature vectors of electroencephalogram signals; obtaining a weight matrix, multiplying each feature vector by the weight matrix to obtain a fused feature; classifying four common emotions, namely happiness, sadness, calmness, and disgust, through a support vector machine; and predicting emotional scores by evolving emotions into four dimensions of pleasure-tension-excitement-certainty and adopting a multivariate non-linear regression method. Compared with the prior art, the present invention has a closer emotion recognition ability to humans through information fusion; realizes the ability of autonomous evolution and continuous adjustment of emotion discrimination by using the form of dynamically updating weight parameters; and discrete and continuous emotion recognition can achieve a more scientific and in-depth description of emotional changes.

To provide a customer experience value analysis device capable of grasping how much an emotional value experienced by a customer is changed before and after an event.SOLUTION: The CX analysis device 10 includes a data acquisition unit 101 that acquires text data including information on a specific facility based on social media information posted on a social media platform, an emotion analysis unit 102 that analyzes an emotion of a poster who has posted the social media information based on a predefined emotional score based on the text data, a CX analysis unit 103 that analyzes a customer experience value based on an analysis result of the emotion, and a processed data display unit 104 that outputs a calculation result of a customer experience value analysis score calculated based on an analysis result of the customer experience value.SELECTED DRAWING: Figure 1

The invention discloses an emotion intervention method and device, a vehicle and a storage medium. The method comprises the steps that video data and audio data of a driver and passengers in the vehicle driving process are acquired; performing emotion state detection on the driver according to the video data and the audio data to obtain an emotion score of the driver; when it is determined that the driver and the passenger are in a negative emotional state according to the emotional score, an intervention strategy is determined according to the emotional score, and the intervention strategy is used for smoothing the negative emotion of the driver and the passenger; and performing emotion intervention on the driver and passengers according to the intervention strategy. According to the method, when it is determined that the driver is in the negative emotional state based on the detected emotional score of the driver in the vehicle driving process, emotional intervention is conducted on the driver according to the emotional score so as to flatten the negative emotions of the driver, the situation that the driver is distracted to drive, and consequently the driving risk of vehicle driving is increased can be avoided, and the driving safety is improved. And the driving safety of vehicle driving can be improved.

The invention provides a product evaluation method and system and a storage medium. The method comprises the following steps: constructing a score matrix according to a product sample image and an expert score; determining key emotion vocabularies in the emotion sample vocabularies according to the scoring matrix, and constructing model training data according to the key emotion vocabularies and the model sample images; performing model training on the product evaluation model according to the model training data until the product evaluation model converges; and inputting a to-be-evaluated product image into the converged product evaluation model for emotion evaluation to obtain an emotion score, and generating a product evaluation result according to the emotion score. According to the embodiment of the invention, model training is carried out on the product evaluation model through the model training data, so that the converged product evaluation model can accurately capture emotion preferences of the user on different product images, emotion evaluation can be effectively carried out on the input to-be-evaluated product image, an emotion score is obtained, and the user experience is improved. And a product evaluation result can be automatically generated based on the emotion score.

We provide the system. [Solution] A natural language processing method for analyzing communication data received from a user, An evaluation method for assigning an emotional score to the analyzed communication data, A selection means that filters communications based on an emotional rating value derived from user settings, A means of providing filtered communications to the user terminal, A learning method that improves filtering accuracy by updating the machine learning model based on adjustments to user selection settings, A receiving means that can identify and display communications that point out new areas for improvement, A system that includes this.

The invention discloses a market quotation prediction method and system based on block chain data and multi-modal fusion, and relates to the technical field of data analysis, by fusing block chain native data, an objective physical state reference is established from a market microstructure level, and the depth and uniqueness of analysis are enhanced; and by capturing and analyzing the original sequential logic triggered by the emotion instead of simple emotion scores, the key insight in the market psychological evolution rhythm is reserved. According to the method, the joint anomaly is judged only when the physical state deviates from the standard and the emotion triggering sequence is abnormal at the same time, the recognition specificity of a real inflection point signal is remarkably improved, noise interference caused by fluctuation of a single factor is effectively filtered, and therefore the reliability of a prediction result is improved.

The application provides a regional data monitoring method and system, wherein the method comprises the following steps: performing semantic extraction on a financial text to obtain an initial semantic vector of each segmented word in the financial text; determining an emotional segmented word and an emotional score of the emotional segmented word in the financial text based on an emotional dictionary, and determining a trend expression word and a trend score of the trend expression word in the financial text based on a trend expression dictionary; strengthening the initial semantic vector of each emotional segmented word based on the emotional score of the emotional segmented word, and strengthening the initial semantic vector of each trend expression word based on the trend score of the trend expression word, to obtain a strengthened semantic vector of the emotional segmented word and the trend expression word; performing regression analysis on the strengthened semantic vector of the emotional segmented word and the trend expression word and the initial semantic vector of the remaining segmented word in the financial text to obtain an emotional score of the financial text; and based on the emotional score of the financial text, statistical target regional financial public opinion data. The application realizes more accurate public opinion data monitoring of a target region.

The application relates to the technical field of data processing, in particular to a communication equipment service quality dynamic evaluation and optimization system based on customer follow-up visits, which comprises an acquisition module, an abnormality judgment module, a cause analysis module, a feature calculation module, a quality evaluation module, an output module and an adjustment module. The application identifies unexpressed attitudes according to the time sequence entropy value of the emotional score sequence; quantifies the deviation degree of explicit expression and implicit emotion through the KL divergence of IVR keys and voice emotion; cross-verification of the follow-up text semantics and the time consumption of the maintenance process is carried out to realize cause tracing; the information content behind the silence is quantified, comprehensive judgment is realized through the calculation of the quality deviation degree; the threshold is dynamically adjusted according to the fluctuation characteristics of the deviation degree to form a closed loop, and the problems of low real experience recognition accuracy and low service quality abnormality cause detection accuracy caused by excessive dependence on single satisfaction score and the inability to recognize complex emotional guidance are effectively solved.

The invention discloses a data method for depressive state monitoring, which comprises the following steps: acquiring personal basic information of a depressive disorder tester, and matching corresponding test content data based on the personal basic information; obtaining response content data of the depression tester, extracting first response emotion feature data, a corresponding emotion type and an emotion score based on a preset deep learning model, and obtaining second response emotion feature data, a corresponding emotion type and an emotion score based on a preset emotion driving model; establishing an emotion matrix of the depression testers according to the emotion types and the emotion scores, and calling a depression emotion matrix in a depression database, and calculating a first depression state evaluation value and a second depression state evaluation value corresponding to the first emotion matrix, the second emotion matrix and the depression emotion matrix, comparing the first depression state evaluation value and the second depression state evaluation value with a preset depression emotion threshold value to obtain a depression comparison result, and judging the mental health state of the depression tester according to the depression comparison result.

The present application relates to the field of computer technology, and is a speech recognition solution, specifically a multimodal interpretation training evaluation method and evaluation device. The embodiment provided by the present invention determines the semantic correctness and emotional tendency of the translation by performing standard text matching and emotional tendency score calculation on the text data respectively, extracts multiple features from the audio data and determines the translation fluency based on the expression of multiple features, and determines the corresponding semantic score, emotional score and fluency score according to the semantic correctness, emotional tendency and translation fluency, and evaluates the trainee's translation behavior according to the semantic score, emotional score and fluency score. The method provided in the embodiment of the present application can realize the automated evaluation of the translation process and results based on natural language processing technology and speech processing technology, and can comprehensively evaluate the results from three aspects: accurate semantic expression, emotional tendency and fluency expression compared with the existing technology.

The invention relates to the technical field of emotion calculation, neuroscience and man-machine interaction crossing, in particular to an emotion calculation-oriented man-machine interaction multi-scale neural mechanism interpretation method, which comprises the following steps of: (1) in a man-machine interaction process, synchronously acquiring multi-channel electroencephalogram and electrocardio physiological data, and acquiring a multi-dimensional subjective emotion score as a label; (2) calculating dynamic function connection between brain intervals, and quantifying a brain collaborative activity mode; meanwhile, heart rate variability characteristics are extracted, and autonomic nervous system response is quantified; and (3) establishing a mapping relation from multi-scale physiological features to multi-dimensional emotion tags by adopting a partial least squares regression model, and identifying key heart-brain interaction indexes through variable projection importance analysis and load analysis so as to realize visualization and deep interpretation of an emotional neural mechanism. According to the method, the core challenges of difficulty in emotion state decoding and opaque internal biological mechanism of an existing emotion calculation technology in a complex human-computer interaction scene can be effectively solved.

The embodiment of the invention discloses a character emotion recognition method and device. The method comprises the steps of performing emotion recognition on audio of a target person, and determining a target emotion type and a corresponding first confidence coefficient; performing key point collision on the face key points in the monitoring picture and the emotion key points; if it is determined that the monitoring picture is suitable for identifying the target emotion type of the target person, capturing the target person, and determining an image emotion score; and determining a character emotion recognition result of the target character according to the first confidence corresponding to the target emotion type and the image emotion score. According to the scheme, based on the key point collision of the face key points and the character face emotion key points in the monitoring picture, image snapshot is carried out when the picture which visually reflects the character emotion is presented in the monitoring picture, the reference of the snapshot image to emotion recognition is improved, and through combined recognition of the audio and the snapshot image, the recognition efficiency is improved. And the emotion recognition accuracy is improved.

The application belongs to the technical field of intelligent voice, and discloses a fund transaction room intelligent voice real-time communication method, system and terminal. The communication method comprises the following steps: first, acquiring fund transaction room multi-source voice data through the terminal, and identifying voice instructions related to the user and time domain, frequency domain, nonlinear dynamics and other emotional expression characteristics; the system background normalizes various emotional characteristics of the user to obtain characteristic scores, and obtains a comprehensive emotional score by weighted summation and determines a comprehensive emotional category; judging the risk level of the voice instruction according to a preset risk control threshold, and sending the comprehensive emotional category and the risk level to the risk control or compliance department; through accurate separation of the voice instruction and the emotional information, the emotional state of the trader is comprehensively quantitatively evaluated, so that the transaction decision is more scientific and reasonable; the risk level is effectively identified, and the warning instruction is accurately determined, so that the transaction risk is reduced.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a service optimization method, device and equipment based on multi-modal data and a storage medium, and the service optimization method comprises the steps: inputting user audio information into an audio emotion classification model, and obtaining a first emotion classification of a user; based on a preset business emotion dictionary, performing emotion scoring on each keyword corresponding to the user text information to obtain an emotion score of each keyword; weighting the scores of the alternative emotion classifications and the emotion scores of the keywords corresponding to the alternative emotion classifications to obtain a second emotion classification with the highest emotion score in the weighted alternative emotion classifications; according to the first emotion classification and the second emotion classification of the user, obtaining a final emotion classification of the user; and generating a service recommendation strategy for the user according to the final sentiment classification of the user. The technical problem that the business recommendation strategy cannot be customized for the user according to the actual emotion of the user in the prior art is solved.

The application belongs to the technical field of intelligent sound boxes, and discloses an emotional intelligent sound box system for a depression group and an emotional intelligent sound box, wherein a user wakes up the system through a self-defined wake-up word with voiceprint recognition, meanwhile, the system can actively judge whether the user is speaking and record the speaking content to complete an instruction, the system determines the depression state of the user by analyzing the emotional score of the user, chats with the user, and relieves the user's psychology, once an abnormality is found, the system enters into monitoring and early warning, and can intelligently control home appliances. The emotional intelligent sound box disclosed by the application overcomes the problems of the absence of a chatting function and stiff conversation in traditional sound boxes, can actively detect whether the user is speaking, greatly improves the user experience, accurately identifies the user emotion through voice recognition and natural language processing technology, and avoids the waste of program resources, meanwhile, the emotional interaction mode is developed for the depression group, effectively improving the communication experience and answer quality of the user.

The present invention relates to the field of computer vision technology, and discloses the construction of an emotion score determination model and an emotion score determination method, device and equipment. The model construction method constructs an emotion score determination model, which is used to characterize the correlation between each first category label score and each second category label score. The first classification method includes multiple first category labels, and the second classification method includes multiple second category labels. When subsequently determining the first category label score of an emotion image to be analyzed, the emotion score determination model can be used to determine the second category label and score corresponding to the emotion image to be analyzed, which solves the defect in the related art that the scoring result of determining the emotion score under the second classification method based on a scale is not accurate enough.

The invention discloses a method and a system for evaluating classroom emotions of students based on limb movement analysis and a medium, and belongs to the technical field of intelligent teaching. Identifying the total number N of students in the picture in real time based on the target detection model; analyzing the current emotional state of the classroom. On the basis of MediaPipe Pose, identifying the action of the student; performing action tracking and counting the number of students of each type of action, and classifying data into a positive behavior data set and a negative behavior data set; based on the positive behavior data set and the negative behavior data set, respectively calculating vector modulus lengths of the positive behavior and the negative behavior; calculating a final emotion score based on the vector modulus lengths of the positive behaviors and the negative behaviors; and according to the final emotional score, determining the overall emotional state of the classroom student. According to the invention, the actions of the students at each time point are analyzed, then the classroom emotion is judged, and the classroom emotion is quantified, so that the classroom emotion can be obtained more conveniently and visually.

The application relates to the technical field of data analysis, and provides a user emotion recognition method and device, electronic equipment and a storage medium, the method comprising the following steps: generating a target emotion change curve according to a historical video call record of a target person acquired in advance, performing trend analysis on the target emotion change curve to obtain an emotion change habit; obtaining real-time call information of the target person, performing dynamic weight division on the real-time call information based on the emotion change habit to obtain an output weight; performing standardization processing on the real-time call information, performing weighted fusion on the processed real-time call information according to the output weight to obtain a real-time emotion score; obtaining a current emotion type of the target person according to the real-time emotion score, and generating a prompt interface according to the current emotion type and displaying the prompt interface on a terminal interface. The application significantly improves the precision, robustness and practical value of emotion recognition, can be widely applied to video call, intelligent customer service and other scenes, and improves the human-computer interaction experience.

The invention discloses a multi-mode mental health prediction method and system, and relates to the technical field of mental health prediction. Determining a target user according to the prediction request, and obtaining target data; inputting the target data into a preset classification model to obtain an emotion recognition result; determining a first group of weight coefficients, a second group of weight coefficients and a third group of weight coefficients according to the emotion recognition result; obtaining a plurality of first feature values corresponding to the voice feature set, and calculating the first feature values and the first group of weight coefficients to obtain a first emotion score; obtaining a plurality of second feature values corresponding to the text feature set, and calculating the second feature values and a second group of weight coefficients to obtain a second emotion score; obtaining a plurality of third feature values corresponding to the image feature set, and calculating the third feature values and a third group of weight coefficients to obtain a third emotion score; and determining a fourth emotion score according to the first emotion score, the second emotion score and the third emotion score, and determining a mental health state corresponding to the target user based on the fourth emotion score.