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 701–725 of 2041 papers

TitleStatusHype
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
Deep CNN with late fusion for realtime multimodal emotion recognition—0
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition—0
Customising General Large Language Models for Specialised Emotion Recognition TasksCode0
Improving Personalisation in Valence and Arousal Prediction using Data Augmentation—0
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations—0
The Power of Properties: Uncovering the Influential Factors in Emotion Classification—0
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios—0
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy ConditionsCode0
Improving Facial Landmark Detection Accuracy and Efficiency with Knowledge Distillation—0
nEMO: Dataset of Emotional Speech in PolishCode0
Dynamic Resolution Guidance for Facial Expression Recognition—0
Music Recommendation Based on Facial Emotion Recognition—0
IITK at SemEval-2024 Task 10: Who is the speaker? Improving Emotion Recognition and Flip Reasoning in Conversations via Speaker Embeddings—0
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach—0
Exploring Emotions in Multi-componential Space using Interactive VR Games—0
Affective-NLI: Towards Accurate and Interpretable Personality Recognition in ConversationCode0
Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT—0
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio DeepfakeCode0
Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages—0
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause—0
Inclusive Design Insights from a Preliminary Image-Based Conversational Search Systems Evaluation—0
Cross-Attention is Not Always Needed: Dynamic Cross-Attention for Audio-Visual Dimensional Emotion Recognition—0
MMCert: Provable Defense against Adversarial Attacks to Multi-modal ModelsCode0
Fusion approaches for emotion recognition from speech using acoustic and text-based features—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