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 691–700 of 2041 papers

Title	Date	Tasks	Status	Hype
Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations	Aug 14, 2023	Action DetectionActivity Detection	CodeCode Available	0
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition	Aug 13, 2023	Contrastive LearningDomain Adaptation	—Unverified	0
ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy	Aug 12, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features	Aug 11, 2023	Continual LearningEmotion Recognition	CodeCode Available	0
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition	Aug 9, 2023	EEGEEG Emotion Recognition	—Unverified	0
Addressing Racial Bias in Facial Emotion Recognition	Aug 9, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition	Aug 8, 2023	Contrastive LearningDisentanglement	—Unverified	0
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition	Aug 8, 2023	AttributeCross-corpus	—Unverified	0
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection	Aug 7, 2023	Continual LearningEmotion Recognition	CodeCode Available	1
Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining	Aug 7, 2023	Emotion RecognitionOpinion Mining	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