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 1571–1580 of 2041 papers

Title	Date	Tasks	Status
MUCS@DravidianLangTech@ACL2022: Ensemble of Logistic Regression Penalties to Identify Emotions in Tamil Text	May 1, 2022	Emotion Recognitionregression	—Unverified
Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition	Sep 7, 2021	Emotion RecognitionFace Model	—Unverified
Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems	Apr 21, 2024	Emotion RecognitionMovie Recommendation	—Unverified
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition	Dec 10, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Multi-Cue Adaptive Emotion Recognition Network	Nov 3, 2021	Emotion Recognition	—Unverified
Multi-face emotion detection for effective Human-Robot Interaction	Jan 13, 2025	Emotion RecognitionFacial Expression Recognition	—Unverified
A New Multilabel System for Automatic Music Emotion Recognition	May 29, 2019	BIG-bench Machine LearningEmotion Recognition	—Unverified
Multi-Label Compound Expression Recognition: C-EXPR Database & Network	Jan 1, 2023	Emotion RecognitionFacial Expression Recognition (FER)	—Unverified
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation	Oct 27, 2023	Emotion RecognitionKnowledge Distillation	—Unverified
Multilevel Transformer For Multimodal Emotion Recognition	Oct 26, 2022	Emotion RecognitionMultimodal Emotion 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