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 791–800 of 2041 papers

Title	Date	Tasks	Status
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis	Dec 17, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Facial Emotion Recognition using CNN in PyTorch	Dec 17, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition	Dec 17, 2023	Contrastive LearningEmotion Recognition	—Unverified
Facial Emotion Recognition in VR Games	Dec 12, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
NAC-TCN: Temporal Convolutional Networks with Causal Dilated Neighborhood Attention for Emotion Understanding	Dec 12, 2023	Emotion Recognition	CodeCode Available
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models	Dec 11, 2023	Emotion RecognitionFairness	—Unverified
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations	Dec 11, 2023	Data AugmentationEmotion Recognition	—Unverified
Speech and Text-Based Emotion Recognizer	Dec 10, 2023	Data AugmentationEmotion Recognition	—Unverified
A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learning	Dec 10, 2023	Emotion Recognition	—Unverified
Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows	Dec 10, 2023	Emotion Recognition	CodeCode Available

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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