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 661–670 of 2041 papers

Title	Date	Tasks	Status	Hype
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023	Sep 11, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations	Sep 9, 2023	Emotion RecognitionKnowledge Distillation	—Unverified	0
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations	Sep 8, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
LanSER: Language-Model Supported Speech Emotion Recognition	Sep 7, 2023	Automatic Speech RecognitionEmotion Recognition	—Unverified	0
Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation	Sep 6, 2023	Data AugmentationEmotion Recognition	—Unverified	0
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition	Sep 5, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches	Sep 5, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
Leveraging Label Information for Multimodal Emotion Recognition	Sep 5, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales	Sep 4, 2023	Emotion Recognition	—Unverified	0
UniSA: Unified Generative Framework for Sentiment Analysis	Sep 4, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	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