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Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

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Showing 751–800 of 2041 papers

TitleStatusHype
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation—0
Emotion Recognition from Speech based on Relevant Feature and Majority Voting—0
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition—0
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition—0
Emotion Recognition from the perspective of Activity Recognition—0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning—0
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis—0
Emotion Recognition in Context—0
Bias in Emotion Recognition with ChatGPT—0
Conversational Transfer Learning for Emotion Recognition—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces—0
Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks—0
Emotion Recognition In Persian Speech Using Deep Neural Networks—0
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild—0
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data—0
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis—0
EEG based Emotion Recognition of Image Stimuli—0
EEG based Emotion Recognition: A Tutorial and Review—0
Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks—0
Emotion recognition techniques with rule based and machine learning approaches—0
Emotion Recognition under Consideration of the Emotion Component Process Model—0
Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features—0
Emotion Recognition Using Convolutional Neural Networks—0
Emotion Recognition Using Fusion of Audio and Video Features—0
Emotion Recognition using Machine Learning and ECG signals—0
Bias and Fairness on Multimodal Emotion Detection Algorithms—0
Emotion Recognition Using Speaker Cues—0
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis—0
Emotion recognition with 4kresolution database—0
Edge Based Grid Super-Imposition for Crowd Emotion Recognition—0
Emotion Recognition with Facial Attention and Objective Activation Functions—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Emotion Recognition with Machine Learning Using EEG Signals—0
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals—0
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling—0
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context—0
Emotions in the Loop: A Survey of Affective Computing for Emotional Support—0
Emotion Stimulus Detection in German News Headlines—0
A Comparison Of Emotion Annotation Schemes And A New Annotated Data Set—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition—0
Emotiphons: Emotion Markers in Conversational Speech - Comparison across Indian Languages—0
EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction—0
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions—0
ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition—0
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training—0
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified