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 871–880 of 2041 papers

Title	Date	Tasks	Status
Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition	Sep 5, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Leveraging Label Information for Multimodal Emotion Recognition	Sep 5, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches	Sep 5, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales	Sep 4, 2023	Emotion Recognition	—Unverified
Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement	Sep 3, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling	Sep 3, 2023	Data AugmentationDisentanglement	—Unverified
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition	Aug 31, 2023	Contrastive LearningEmotion Recognition	—Unverified
An Emotion Recognition Embedded System using a Lightweight Deep Learning Model	Aug 31, 2023	Deep LearningEEG	CodeCode Available
Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children	Aug 31, 2023	Audio Emotion RecognitionEmotional Intelligence	—Unverified
AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories	Aug 29, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified

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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