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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 401–425 of 2041 papers

TitleStatusHype
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis—0
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms—0
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions—0
Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages—0
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion RecognitionCode0
Unifying EEG and Speech for Emotion Recognition: A Two-Step Joint Learning Framework for Handling Missing EEG Data During Inference—0
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics—0
United we stand, Divided we fall: Handling Weak Complementary Relationships for Audio-Visual Emotion Recognition in Valence-Arousal Space—0
Compound Expression Recognition via Large Vision-Language Models—0
Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal SpaceCode0
Emotion Recognition with CLIP and Sequential Learning—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion RecognitionCode0
Heterogeneous bimodal attention fusion for speech emotion recognition—0
Multimodal Emotion Recognition and Sentiment Analysis in Multi-Party Conversation Contexts—0
Bimodal Connection Attention Fusion for Speech Emotion Recognition—0
Personalized Emotion Detection from Floor Vibrations Induced by Footsteps—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition—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