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 1971–1980 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis	Apr 29, 2025	Emotion Recognition	—Unverified
Emotion Recognition in Context	Jul 1, 2017	Emotion RecognitionEmotion Recognition in Context	—Unverified
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances	May 8, 2019	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Conversational Transfer Learning for Emotion Recognition	Oct 11, 2019	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Emotion Recognition in Conversation using Probabilistic Soft Logic	Jul 14, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods	Aug 28, 2019	Emotion Recognitionfeature selection	—Unverified
Emotion Recognition In Persian Speech Using Deep Neural Networks	Apr 28, 2022	BIG-bench Machine LearningEmotion Recognition	—Unverified
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild	Aug 16, 2018	Emotion RecognitionFacial Emotion Recognition	—Unverified
Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks	Feb 29, 2020	Emotion Recognition	—Unverified
Emotion recognition techniques with rule based and machine learning approaches	Feb 28, 2021	BIG-bench Machine LearningCultural Vocal Bursts Intensity Prediction	—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