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 881–890 of 2041 papers

Title	Date	Tasks	Status
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus	Jun 12, 2023	Decision MakingEmotion Recognition	—Unverified
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets	Dec 10, 2020	Deep LearningEmotion Classification	—Unverified
Spatiotemporal Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment	Apr 29, 2025	Dynamic Time WarpingEmotion Recognition	—Unverified
Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach	Nov 9, 2023	Emotion Recognition	—Unverified
Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition	Dec 27, 2020	Emotion RecognitionFacial Expression Recognition (FER)	—Unverified
Exploring Emotions in Multi-componential Space using Interactive VR Games	Apr 4, 2024	Emotion Recognition	—Unverified
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis	Apr 5, 2019	Emotion ClassificationEmotion Recognition	—Unverified
Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition	Mar 15, 2023	Emotion RecognitionFace Recognition	—Unverified
Convolutional Speech Recognition with Pitch and Voice Quality Features	Sep 2, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation	Dec 4, 2024	Emotion RecognitionEmotion Recognition in Conversation	—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