SOTAVerified

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 1026–1050 of 2041 papers

TitleStatusHype
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition—0
Combining Heterogeneous User Generated Data to Sense Well-being—0
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels—0
ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces—0
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data—0
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition—0
Comparison of Gender- and Speaker-adaptive Emotion Recognition—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation—0
Compound Expression Recognition via Large Vision-Language Models—0
Computational Emotion Analysis From Images: Recent Advances and Future Directions—0
Computationally Efficient Wasserstein Loss for Structured Labels—0
ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition—0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition—0
Construction and Annotation of a French Folkstale Corpus—0
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions—0
Construction of Emotional Lexicon Using Potts Model—0
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas—0
Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition—0
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias—0
Context-aware Cascade Attention-based RNN for Video Emotion Recognition—0
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition—0
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness—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