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 901–910 of 2041 papers

Title	Date	Tasks	Status
Adaptive Fusion Techniques for Multimodal Data	Nov 10, 2019	Emotion RecognitionMachine Translation	—Unverified
Expression Recognition Analysis in the Wild	Jan 22, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents	Aug 30, 2022	Emotion Recognition	—Unverified
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer	Jul 8, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Extending RNN-T-based speech recognition systems with emotion and language classification	Jul 28, 2022	Emotion ClassificationEmotion Recognition	—Unverified
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network	Oct 15, 2019	Emotion RecognitionFew-Shot Learning	—Unverified
Face Detection on Mobile: Five Implementations and Analysis	May 11, 2022	Emotion RecognitionFace Detection	—Unverified
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition	Mar 24, 2025	Domain AdaptationEEG	—Unverified
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Apr 18, 2021	Adversarial AttackAutomatic Speech Recognition	—Unverified
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets	Jul 23, 2019	Data AugmentationEmotion 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