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 376–400 of 2041 papers

TitleStatusHype
VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge InjectionCode0
Emotions in the Loop: A Survey of Affective Computing for Emotional Support—0
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech RecognitionCode0
Spatiotemporal Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment—0
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis—0
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks—0
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities—0
Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach—0
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics—0
Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English—0
Visual and textual prompts for enhancing emotion recognition in video—0
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System—0
Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming—0
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis—0
Attributes-aware Visual Emotion Representation Learning—0
Leveraging Label Potential for Enhanced Multimodal Emotion Recognition—0
Emotion Recognition Using Convolutional Neural Networks—0
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation—0
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion—0
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP—0
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training—0
Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis—0
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering—0
OmniVox: Zero-Shot Emotion Recognition with Omni-LLMs—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—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