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

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Showing 1531–1540 of 2041 papers

Title	Date	Tasks	Status
Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey	Mar 9, 2024	Emotion Recognition	—Unverified
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations	Dec 1, 2020	Emotion RecognitionSentiment Analysis	—Unverified
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis	Nov 18, 2024	Emotion RecognitionSentiment Analysis	—Unverified
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition	Oct 27, 2021	DiversityEmotion Classification	—Unverified
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes	Dec 1, 2020	Emotion RecognitionTransfer Learning	—Unverified
MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition	Jul 8, 2024	BenchmarkingDeep Learning	—Unverified
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning	Dec 30, 2024	Emotion RecognitionMulti-Task Learning	—Unverified
Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning	May 22, 2025	Emotion RecognitionMeta-Learning	—Unverified
Meta Transfer Learning for Emotion Recognition	Jun 23, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Meta Transfer Learning for Facial Emotion Recognition	May 25, 2018	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