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 991–1000 of 2041 papers

Title	Date	Tasks	Status
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT	Dec 1, 2020	Emotion Recognition	—Unverified
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations	Mar 26, 2025	cross-modal alignmentEmotion Classification	—Unverified
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures	Oct 13, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations	Oct 6, 2023	Emotion RecognitionTransfer Learning	—Unverified
Gaze-enhanced Crossmodal Embeddings for Emotion Recognition	Apr 30, 2022	Emotion ClassificationEmotion Recognition	—Unverified
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis	Oct 2, 2024	Emotion RecognitionGraph Sampling	—Unverified
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition	Jun 13, 2023	AttributeContrastive Learning	—Unverified
General Purpose Textual Sentiment Analysis and Emotion Detection Tools	Sep 11, 2013	Emotion RecognitionOpinion Mining	—Unverified
A Bilingual Attention Network for Code-switched Emotion Prediction	Dec 1, 2016	Emotion Recognition	—Unverified
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models	Sep 20, 2024	Emotional IntelligenceEmotion 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