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 1791–1800 of 2041 papers

Title	Date	Tasks	Status
Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly	Nov 9, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified
Deep Evolution for Facial Emotion Recognition	Sep 29, 2020	Emotion RecognitionEvolutionary Algorithms	—Unverified
Deep factorization for speech signal	Feb 27, 2018	Emotion RecognitionSpeaker Recognition	—Unverified
Deep Factorization for Speech Signal	Jun 5, 2017	Emotion Recognition	—Unverified
Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition	Jan 15, 2019	Emotion RecognitionVideo Emotion Recognition	—Unverified
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations	Dec 11, 2023	Data AugmentationEmotion Recognition	—Unverified
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition	Feb 17, 2023	Cross-corpusEmotion Recognition	—Unverified
Deep Learning as Feature Encoding for Emotion Recognition	Oct 30, 2018	Deep LearningEmotion Recognition	—Unverified
Deep learning for affective computing: text-based emotion recognition in decision support	Mar 16, 2018	Decision MakingEmotion Recognition	—Unverified
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms	Mar 25, 2025	Deep LearningEmotion 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