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 1091–1100 of 2041 papers

Title	Date	Tasks	Status	Hype
TRILLsson: Distilled Universal Paralinguistic Speech Representations	Mar 1, 2022	Emotion RecognitionKnowledge Distillation	—Unverified	0
Towards a Common Speech Analysis Engine	Mar 1, 2022	Emotion RecognitionLanguage Identification	—Unverified	0
DAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition	Feb 27, 2022	EEGElectroencephalogram (EEG)	—Unverified	0
Novel techniques for improving NNetEn entropy calculation for short and noisy time series	Feb 25, 2022	EEGElectroencephalogram (EEG)	—Unverified	0
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals	Feb 21, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	1
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation	Feb 20, 2022	Domain AdaptationEEG	CodeCode Available	0
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses	Feb 19, 2022	DescriptiveEmotion Recognition	CodeCode Available	1
Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?	Feb 18, 2022	Emotion ClassificationEmotion Recognition	CodeCode Available	1
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models	Feb 16, 2022	Data AugmentationEmotional Intelligence	—Unverified	0
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition	Feb 11, 2022	Data AugmentationEEG	CodeCode Available	1

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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