129 results about "Emotion detection" 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.

A method of providing emotive text-to-speech includes obtaining input text characterizing a natural language response generated by an assistant LLM to a query input by a user during a conversation between the user and the assistant LLM, and processing, using the assistant LLM, the input text conditioned on an emotion detection task prompt to predict, as output from the assistant LLM, an emotional state of the natural language response. The method also includes determining, based on the emotional state of the natural language response predicted as output from the assistant LLM, an emotional embedding for the input text and instructing a TTS model to process the input text and the emotional embedding to generate a synthesized speech representation of the natural language response conveying the emotional state of the natural language response as specified by the emotional embedding.

The present invention proposes an AI dynamic emotion recognition multi-dimensional targeted training system, which relates to the fields of computer vision technology and artificial intelligence technology. It includes multiple emotion collection and training terminals at the front end and a background server. The emotion collection and training terminals are used to detect the emotions of the tested personnel. The terminals analyze the face images to obtain the multi-dimensional emotion data of the tested personnel. The multi-dimensional emotion data is uploaded to the background server. The background server analyzes the current emotion state of the tested personnel according to the received multi-dimensional emotion data, and pushes different targeted training tasks to the corresponding emotion collection and training terminals for the tested personnel in different states. After receiving the targeted training tasks, the emotion collection and training terminals, the tested personnel complete the corresponding training content according to the guidance. The present invention can specifically improve the psychological quality of the tested personnel or improve the bad emotion state.

An AI-driven emotion recognition system for learning adaptation in educational institutions, comprehensive: • an emotion recognition unit that tracks students' facial expressions in real time and categorizes the resulting expressions into a specific emotional class from a given set using a Convolutional Neural Network (CNN) trained on a large emotion dataset; • a real-time processing unit that extracts facial features and dynamic emotional characteristics of students through adaptive filter algorithms; • an LMS integration unit, adapted to combine discovered emotional knowledge with online and offline learning environments for customizable learning adjustments; • a personalization engine programmed to intelligently adapt training content, response approaches, and training materials according to the identified emotional states, so that each learner receives individually tailored and more intensive learning experiences; • a feedback loop that sends educators real-time alerts and recommended videos to address emotional feedback or lesson gaps, allowing teachers to tackle negative signals in the moment (e.g., frustrated face, etc.); • AI data storage and adaptive learning model unit that stores anonymized data for emotion recognition, updates the AI ​​model and improves it through learning over time, and ensures that the use of the information is in compliance with data protection regulations; • A user interface module specifically designed for educators, providing a visual overview of students' emotional patterns and real-time analysis of their emotions and engagement as a basis for important decisions that lead to improvements in teaching processes.

The invention relates to the technical field of artificial intelligence, and particularly provides a multi-modal information fusion emotion detection method based on visible light and voiceprint. The method comprises the following steps: respectively acquiring video images and environment sounds of old people in a monitoring environment through a visible light image module and an audio voiceprint acquisition module; a facial expression detection module is used for recognizing a human face in the video image, and a visual anomaly signal is output; recognizing an audio voiceprint signal in the environmental sound according to a voiceprint detection module, and outputting an audio abnormal signal; according to the method, the detection accuracy is effectively improved, the missing report is reduced, the method is suitable for home and old-age care institution environments, and the method is of great significance to guarantee the safety of old people and alleviate serious consequences caused by falling down.

The invention discloses a high-precision facial expression emotion detection method based on improved YOLOv8n, which optimizes a facial expression recognition task while retaining a basic framework of the YOLOv8n, and comprises the following steps of: firstly, replacing a standard convolution module in a backbone network and a neck structure of the YOLOv8n with a self-adaptive multi-branch convolution module; a large-scale separable nuclear attention module is integrated to the seventh layer and the tenth layer of a YOLOv8n backbone network, dynamic attention on key facial features is enhanced, interlayer feature injection connection is applied to the YOLOv8n backbone network and a neck structure, cross-layer connection between the sixth layer and the seventeenth layer and cross-layer connection between the ninth layer and the nineteenth layer are established, and therefore the dynamic attention on the key facial features is enhanced. By establishing the direct connection between the shallow and deep features, the information loss is reduced, the multi-scale feature fusion is optimized, and the precision and efficiency of facial expression emotion detection are significantly improved.

The invention provides an emotion recognition method and device. The method comprises the following steps: acquiring face video data and environment data; extracting image data, voice data and text data according to the collected face video data; extracting image features and physiological signal features from the extracted image data; extracting audio features from the extracted voice data; extracting text features from the extracted text data; environment features are extracted from the collected environment data; splicing and fusing the extracted image features, physiological signal features, audio features, text features and environment features by using an attention mechanism to generate multi-modal fusion features; and inputting the generated multi-modal fusion features into a pre-trained emotion detection model for emotion recognition, and outputting emotion contents to which the multi-modal fusion features belong. The emotion recognition method can solve the problem that an existing emotion recognition method only uses a single-mode picture and ignores other mode data.

The invention relates to the technical field of emotion analysis, in particular to an emotion detection model training method and system based on Transform architecture and a storage medium. The emotion detection model training method based on the Transform architecture provided by the invention has an excellent self-attention mechanism, and can process complex language features and emotion expressions. According to the method, at least two data enhancement operations are defined, independent application probability and adjustment parameters are set for each operation, and the enhancement operations are dynamically selected and applied in the training process, so that the diversity of training data can be increased in a more targeted manner, and the model is more accurate and better in robustness when facing different emotion expression modes. By periodically calculating emotion prediction results and confidence difference values on a verification set and adjusting initial hyper-parameters and updating an enhanced data set according to the difference values, the mechanism enables the model to be adaptively optimized and continuously improved, and high efficiency of the training process is ensured.

Techniques for image scaling based on emotion detection are described. In some embodiments, the techniques include acquiring one or more images of a user, processing the one more images to generate emotion-specific three-dimensional (3D) positions of ears of the user based on a 3D head geometry and an emotion of the user, where the emotion is identified based on the one or more images of the user, and processing one or more audio signals to generate one or more processed audio signals based on the three-dimensional positions of the ears. Further embodiments include systems and non-transitory computer-readable media that perform the steps of the method.

The invention discloses a driver road rage emotion detection method based on machine learning. The method comprises the steps that a, a driver facial expression image is collected through an in-vehicle camera; b, analyzing a voice signal of the driver through a voice recognition system; c, physiological signals such as the heart rate and the breathing rate of the driver are collected through a sensor; d, collecting traffic condition information around the vehicle by using an environment sensing module; e, performing feature extraction on the data obtained in the steps a to d; f, inputting the features extracted in the step e into a pre-trained multi-modal fusion model to identify whether the driver is in a road rage emotional state; g, if the result of the step f indicates that the driver is in the road rage emotion state, corresponding intervention measures are triggered, the multi-modal data fusion and feature extraction technology is used, subtle changes in facial expressions and voice can be recognized more accurately, and the accuracy and reliability of road rage emotion detection are improved.

The invention provides a family-centered child development platform designed to support and enhance a child's academic, emotional, social, and cultural growth. By utilizing an AI-driven system for personalized learning, immersive VR / AR technologies for engaging educational experiences, and a community-building module for peer interaction, it offers an integrated solution for holistic child development. The system incorporates emotion detection, biometric feedback, and emotion-driven content adaptation, which dynamically adjusts the child's learning experience based on real-time emotional and physiological data. Additionally, the platform features culturally relevant content, promotes intergenerational collaboration, and includes a gamified learning environment to foster motivation and engagement. A secure interaction system ensures safe online communication, while the adaptive challenge system maintains an optimal level of difficulty tailored to the child's evolving needs. By combining these technologies, the platform provides a personalized, interactive, and supportive learning journey for children, supported by parents and educators.

The invention is suitable for the technical field of emotion detection, and provides an anxiety risk detection system based on multi-scale time sequence feature fusion, and the system comprises a short-term feature obtaining module which is used for obtaining short-term branch features of multi-modal data of a user; the short-term branch features are used for describing psychological state changes of the user in a recent short time period; the long-term feature acquisition module is used for acquiring long-term branch features of the multi-modal data; the long-term branch features are used for describing a stable psychological trend of the user in a long time period; the attention fusion module is used for fusing the short-term branch features and the long-term branch features based on an attention mechanism to obtain a fusion result; and the detection module is used for processing the fusion result to obtain an anxiety risk detection result of the user. The accuracy and interpretability of anxiety risk detection can be improved.

The embodiment of the invention provides an emotion detection method based on a multi-mode electroencephalogram eye movement signal. According to the method, a dual-path electroencephalogram modal encoder is used for determining time sequence characteristics of an electroencephalogram in a time domain path, determining frequency domain characteristics of an amplitude spectrum of the electroencephalogram in a frequency domain path, and obtaining dual-path time domain and frequency domain fused electroencephalogram characteristics based on the time sequence characteristics and the frequency domain characteristics; the dual-path eye movement modal encoder is used for determining pupil features of the eye movement data in the pupil diameter path, determining eye movement fixation features of the eye movement data in the fixation point path, and obtaining fused eye movement features based on the pupil features and the eye movement fixation features; and the multi-modal expert mixing module is used for dynamically performing modal calling on the multi-modal mixed characteristics which are subjected to modal alignment and are fused with the electroencephalogram characteristics and the eye movement characteristics, performing emotion detection by utilizing an expert with a modal corresponding to the multi-modal mixed characteristics, and outputting an emotion classification result. According to the embodiment of the invention, alignment is realized through multi-task pre-training, and the depressive emotion is accurately recognized.

Apparatuses, systems, and methods for low-latency audio-to-face animation with emotion detection are disclosed herein. The system may receive a first audio stream associated with a first device and a second audio stream associated with a second device, and provide, concurrently, a first segment of the first audio stream and a second segment of the second audio stream as inputs to an emotion detection artificial intelligence (AI) model to obtain first emotion data and second emotion data. The system may then provide, concurrently, a third segment of the first audio stream with the first emotion data and a fourth segment of the second audio stream with the second emotion data as inputs to a face animation AI model to obtain first face pose data and second face pose data, and provide the first face pose data to the first device and the second face pose data to the second device.

The invention relates to the technical field of artificial intelligence, in particular to an edge end real-time voice emotion detection method and system and a storage medium, and the method comprises the steps: obtaining real-time voice data of a user end; performing feature extraction on the voice data by adopting a preset integrated code module to obtain a multi-dimensional acoustic feature vector; inputting the multi-dimensional acoustic feature vector into an emotion recognition model embedded in the preset integrated code, and detecting the emotion of a user to which the user side belongs to obtain a real-time emotion state; and based on the real-time emotional state, executing an interaction operation matched with the user side. According to the method, the voice data is locally processed and does not need to be uploaded to the cloud server, so that the risk of user privacy disclosure is greatly reduced, the computing and bandwidth resource requirements on the cloud server are reduced, and the operation cost of a service provider is reduced.

An object of a system according to an embodiment is to detect an emotion of a user and provide appropriate advice on the basis of the detected emotion.SOLUTION: A system according to an embodiment includes a smart emotion detection speaker, an emotion detection AI, and a mentoring generation AI. The smart speaker detects emotions from the user's voice and speech. The emotion detection AI generates words based on emotions. The mentoring generation AI listens to the user's distress and provides advice.SELECTED DRAWING: Figure 1

Techniques for image scaling based on emotion detection are described. In some embodiments, the techniques include acquiring one or more images of a user, processing the one more images to generate emotion-specific three-dimensional (3D) positions of ears of the user based on a 3D head geometry and an emotion of the user, where the emotion is identified based on the one or more images of the user, and processing one or more audio signals to generate one or more processed audio signals based on the three-dimensional positions of the ears. Further embodiments include systems and non-transitory computer-readable media that perform the steps of the method.

The invention discloses a dialogue generation method and device, a customer service system, a storage medium and a program product, and the method comprises the steps: receiving request data inputted by a user, and carrying out the data preprocessing of the request data; performing intention analysis and emotion detection on the preprocessed request data to obtain user intention information and a user emotion state; performing data search on the user intention information to obtain problem analysis data; generating an initial reply text according to the user intention information and the question analysis data; and adjusting the initial reply text based on the emotional state to obtain a dialogue text. By adopting the embodiment of the invention, the intention and the perception emotional state of the user can be accurately analyzed, and multi-modal interaction is realized by fusing multi-element information, so that real-time response and efficient solution of questions of the user are realized.

The application provides an emotion detection method and device, and the method comprises the following steps: obtaining current dynamic category information of a target object; establishing a current three-dimensional dynamic portrait model of the target object according to the current dynamic category information of the target object; obtaining a current dynamic category result of the target object according to the current three-dimensional dynamic portrait model and a pre-trained emotion detection model; and prompting according to the current dynamic category result. The technical scheme of the application can obtain more accurate and less error results when detecting the unknown dynamic category of the target object, improve the detection efficiency, and improve the user experience.

To provide a technique for properly identifying a change factor of an emotion of a subject contributing to community building, facility building, etc.SOLUTION: An exemplary analysis device detects an emotion of a subject according to the movement of the subject, detects an emotion change position where the emotion has changed, based on the detected emotion, and identifies environmental information corresponding to the emotion change position as change factor information of the emotion.SELECTED DRAWING: Figure 2

The application discloses an emotion detection method and device, a terminal equipment and a readable storage medium, the method comprises the following steps: acquiring audio data, and converting the audio data into initial text data; splitting the initial text data into first text data and second text data, and adding bag labeling to the first text data; inputting the second text data into a preset language correction model to obtain third text data; inputting the third text data into an emotion detection model to detect emotion parameters corresponding to the third text data; based on the fact that the sample information collected by the bank outlets is inaccurate due to noise and noise, and single-sentence-level corpus labeling is difficult, the language model based on BERT is used to intelligently correct the corpus, the influence of noise data on the detection result is reduced, the mapping relationship from a paragraph to a single sentence is constructed through the emotion detection model, and finally the accuracy of the emotion detection of the bank outlets is improved.

The application discloses an emotion detection method in the field of artificial intelligence. The method comprises the following steps: acquiring a PPG signal of a pulse of a user through a PPG sensor; sending the PPG signal to a cloud server; receiving a user emotion state value sent by the cloud server, wherein the user emotion state value is obtained by processing a physiological feature vector according to a neural network model after the cloud server determines the physiological feature vector according to the PPG signal, and the physiological feature vector comprises blood oxygen saturation, heart rate and respiratory rate; and outputting a user emotion state according to the user emotion state value. The emotion detection method can improve emotion detection accuracy. The application also discloses a device capable of implementing the above emotion detection method.

A method of providing emotive text-to-speech includes obtaining input text characterizing a natural language response generated by an assistant LLM to a query input by a user during a conversation between the user and the assistant LLM, and processing, using the assistant LLM, the input text conditioned on an emotion detection task prompt to predict, as output from the assistant LLM, an emotional state of the natural language response. The method also includes determining, based on the emotional state of the natural language response predicted as output from the assistant LLM, an emotional embedding for the input text and instructing a TTS model to process the input text and the emotional embedding to generate a synthesized speech representation of the natural language response conveying the emotional state of the natural language response as specified by the emotional embedding.

The present invention discloses a control method, a computer device and a storage medium for an in-vehicle atmosphere adjustment device, including steps such as detecting the emotions of the driver and passengers to obtain first driver emotion information and first passenger emotion information, adjusting the first driver emotion information according to the first passenger emotion information to obtain first adjusted emotion information and generating a first control instruction, and controlling a first in-vehicle atmosphere adjustment device in the area where the driver is located with the first control instruction. The present invention combines the influence of the emotions of the passengers in the same vehicle space as the driver on the driver, adjusts the atmosphere to be presented by the first in-vehicle atmosphere adjustment device to the driver, and the introduction of the emotion variables of other passengers in the vehicle is beneficial to controlling the atmosphere created by the first in-vehicle atmosphere adjustment device for the driver to be closer to the needs of the driver to calm down bad emotions, is beneficial to the driver calming down bad emotions as soon as possible, and is beneficial to maintaining driving safety. The present invention is widely applied to the field of automotive technology.

To reduce unconscious or unintended bias in evaluating the output of a machine learning (ML) model using reinforcement learning from human feedback, the emotions of a test human evaluating the model output are used in addition to or in lieu of evaluation input to train the model. As an example, if the sensed emotions do not match the evaluation input, the evaluation input may be discounted including discarding it altogether.

A digital character emotion response system is disclosed, incorporating modules configured to process audio and video streams to predict emotional states. The system features an audio processing module, a video processing module, a fusion module for integrating audio and video features, and a machine learning module with an LSTM model for analyzing the combined data to predict and output emotional states with confidence scores.

The invention discloses a multi-modal abnormal emotion recognition method based on multitask, mixed data enhancement and contrast feature decomposition, and the method comprises the steps: transcribing voice data collected in psychological health census into text data, carrying out the feature extraction of the voice data and the text data, obtaining a word embedding sequence and frame-level acoustic features, and forming a sample; performing data enhancement on the word embedding sequence and the frame-level acoustic features to generate a diversified training sample; respectively constructing a text coding model and a voice coding model, and respectively training the corresponding coding models through word embedding sequences and frame-level acoustic features in the diversified training samples; carrying out dimension expansion on the obtained word embedding sequence and the frame-level acoustic features in a copying mode, and forming an abnormal emotion detection training sample by the expanded word embedding sequence and the frame-level acoustic features; and establishing a multi-modal abnormal emotion recognition model, and carrying out migration training on the multi-modal abnormal emotion recognition model through the abnormal emotion detection training sample.

The embodiment of the invention discloses an emotion detection method and device, electronic equipment and a storage medium. The method comprises the following steps: under the condition that to-be-detected media information is obtained, performing feature extraction operation on the to-be-detected media information to obtain feature information of the to-be-detected media information; respectively inputting the feature information into different pre-trained emotion detection models to obtain an output result of each emotion detection model; wherein the output result comprises an association degree value between the to-be-detected media information and each preset emotion type; and determining a target sentiment classification of the to-be-detected media information based on the predetermined decision weight and the output result corresponding to each sentiment detection model. According to the technical scheme provided by the embodiment of the invention, the detection deviation generated when a single emotion detection model is used for detection can be reduced, and the accuracy and robustness of emotion detection can be improved.

A method includes receiving a speech input signal of a speaker; preprocessing the speech signal to generate a preprocessed speech signal; detecting, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker.