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 1011–1020 of 2041 papers

Title	Date	Tasks	Status
Benchmarking Multimodal Sentiment Analysis	Jul 29, 2017	BenchmarkingEmotion Recognition	—Unverified
Advancing Audio Emotion and Intent Recognition with Large Pre-Trained Models and Bayesian Inference	Oct 16, 2023	Bayesian InferenceEmotion Recognition	—Unverified
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis	Jul 30, 2024	EEGEmotion Recognition	—Unverified
DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition	Dec 25, 2023	DisentanglementEmotion Recognition	—Unverified
Benchmarking Domain Generalization on EEG-based Emotion Recognition	Apr 18, 2022	BenchmarkingDomain Adaptation	—Unverified
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition	Oct 22, 2023	EEGEmotion Recognition	—Unverified
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation	Mar 31, 2025	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Graph Neural Network-based EEG Classification: A Survey	Oct 3, 2023	ClassificationEEG	—Unverified
AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition	Jun 9, 2022	Emotion RecognitionRepresentation Learning	—Unverified
An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems	Sep 26, 2022	Decision MakingEmotion Recognition	—Unverified

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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