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 1551–1560 of 2041 papers

Title	Date	Tasks	Status
Mitigating Group Bias in Federated Learning for Heterogeneous Devices	Sep 13, 2023	Decision MakingEmotion Recognition	—Unverified
Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach	May 20, 2025	Emotion RecognitionFairness	—Unverified
MixedEmotions: Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets	May 1, 2015	Emotion RecognitionKnowledge Graphs	—Unverified
MMDS: A Multimodal Medical Diagnosis System Integrating Image Analysis and Knowledge-based Departmental Consultation	Oct 20, 2024	Emotion RecognitionFacial Emotion Recognition	—Unverified
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation	Jun 1, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data	Oct 22, 2023	Emotional IntelligenceEmotion Recognition	—Unverified
Modality-based Factorization for Multimodal Fusion	Nov 30, 2018	Emotion RecognitionPersonality Trait Recognition	—Unverified
Modality Influence in Multimodal Machine Learning	Jun 10, 2023	Decision MakingEmotion Recognition	—Unverified
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks	Oct 31, 2019	Cross-corpusEmotion Recognition	—Unverified
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions	Mar 24, 2025	Emotion RecognitionOverall - Test	—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