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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 751–775 of 2041 papers

TitleStatusHype
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition—0
Emotion Recognition from Speech based on Relevant Feature and Majority Voting—0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning—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
Bias in Emotion Recognition with ChatGPT—0
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis—0
Emotion Recognition in Context—0
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances—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
Bias and Fairness on Multimodal Emotion Detection Algorithms—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