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 1021–1030 of 2041 papers

Title	Date	Tasks	Status
Multi-Task Self-Supervised Pre-Training for Music Classification	Feb 5, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers	Jun 24, 2022	Emotion RecognitionEvent Detection	—Unverified
Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition	Nov 8, 2018	Emotion Recognition	—Unverified
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks	Oct 16, 2024	Age EstimationEmotion Recognition	—Unverified
Multi-Window Data Augmentation Approach for Speech Emotion Recognition	Oct 19, 2020	Data AugmentationEmotion Recognition	—Unverified
MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations	Mar 27, 2019	Emotion Recognition	—Unverified
MUSER: MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task	May 17, 2021	Emotion RecognitionMulti-Task Learning	—Unverified
Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning	Jun 4, 2021	BIG-bench Machine LearningEmotion Classification	—Unverified
Music Interpretation and Emotion Perception: A Computational and Neurophysiological Investigation	May 16, 2025	Emotion Recognition	—Unverified
Music Recommendation Based on Facial Emotion Recognition	Apr 6, 2024	Emotion ClassificationEmotion 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