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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 71–80 of 2041 papers

Title	Date	Tasks	Status	Hype
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition	Apr 30, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Spatiotemporal Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment	Apr 29, 2025	Dynamic Time WarpingEmotion Recognition	—Unverified	0
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis	Apr 29, 2025	Emotion Recognition	—Unverified	0
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks	Apr 29, 2025	Contrastive LearningEEG	—Unverified	0
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities	Apr 28, 2025	EEGEmotion Recognition	—Unverified	0
Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach	Apr 28, 2025	Emotion Recognition	—Unverified	0
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics	Apr 27, 2025	ArticlesEmotion Recognition	—Unverified	0
Visual and textual prompts for enhancing emotion recognition in video	Apr 24, 2025	Emotion RecognitionVideo Emotion Recognition	—Unverified	0
Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English	Apr 24, 2025	Emotion RecognitionHope Speech Detection	—Unverified	0
PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition	Apr 24, 2025	Contrastive LearningEEG	CodeCode Available	1

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All datasets Emomusic RAVDESS EMOTIC FER2013 MaSaC_ERC MPED MSP-Podcast SEED คลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	M2D-CLAP	EmoA	77.4	—	Unverified
2	M2D2	EmoA	76.7	—	Unverified
3	M2D	EmoA	76.1	—	Unverified
4	Jukebox (Pre-training: CALM)	EmoA	72.1	—	Unverified
5	CLMR (Pre-training: contrastive)	EmoA	67.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+Attention	Accuracy	86.7	—	Unverified
2	MultiMAE-DER	WAR	83.61	—	Unverified
3	Intermediate-Attention-Fusion	Accuracy	81.58	—	Unverified
4	Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedST	Accuracy	80.08	—	Unverified
5	ERANN-0-4	Accuracy	74.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAGE	Top-3 Accuracy (%)	14.73	—	Unverified
2	FocusCLIP	Top-3 Accuracy (%)	13.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VGG based	5-class test accuracy	66.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MaSaC-ERC-Z	F1-score (Weighted)	51.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BiHDM	Accuracy	40.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	w2v2-L-robust-12	Concordance correlation coefficient (CCC)	0.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	4D-aNN	Accuracy	96.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	1'"	1	—	Unverified