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 791–800 of 2041 papers

Title	Date	Tasks	Status	Hype
Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations	Apr 17, 2023	ClassificationEmotion Recognition	CodeCode Available	0
Recursive Joint Attention for Audio-Visual Fusion in Regression based Emotion Recognition	Apr 17, 2023	Emotion Recognitionregression	CodeCode Available	1
Evaluation of Speaker Anonymization on Emotional Speech	Apr 15, 2023	Automatic Speech RecognitionEmotion Recognition	—Unverified	0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition	Apr 14, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
Neuromorphic Event-based Facial Expression Recognition	Apr 13, 2023	Emotion RecognitionFacial Expression Recognition	CodeCode Available	1
How you feelin'? Learning Emotions and Mental States in Movie Scenes	Apr 12, 2023	Emotion RecognitionMulti-Label Classification	CodeCode Available	1
Unsupervised Speech Representation Pooling Using Vector Quantization	Apr 8, 2023	Emotion Recognitionintent-classification	CodeCode Available	0
An Empirical Study and Improvement for Speech Emotion Recognition	Apr 8, 2023	Emotion RecognitionMultimodal Emotion Recognition	—Unverified	0
Towards Interpretable Mental Health Analysis with Large Language Models	Apr 6, 2023	Causal Emotion EntailmentEmotion Recognition	CodeCode Available	2
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset	Apr 6, 2023	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	0

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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