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 1076–1100 of 2041 papers

TitleStatusHype
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition—0
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model—0
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach—0
Robust Emotion Recognition in Context Debiasing—0
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition—0
Robust Latent Representations via Cross-Modal Translation and Alignment—0
SAFER: Situation Aware Facial Emotion Recognition—0
Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations—0
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection—0
ScalingNet: extracting features from raw EEG data for emotion recognition—0
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring—0
Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition—0
Seeking Subjectivity in Visual Emotion Distribution Learning—0
Selective Co-occurrences for Word-Emotion Association—0
Self context-aware emotion perception on human-robot interaction—0
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts—0
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
Self-Supervised Learning for Audio-Based Emotion Recognition—0
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Self-supervised representations in speech-based depression detection—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