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 1281–1290 of 2041 papers

Title	Date	Tasks	Status
The Labeled Multiple Canonical Correlation Analysis for Information Fusion	Feb 28, 2021	Emotion RecognitionFace Recognition	—Unverified
Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning	Mar 27, 2024	Emotion RecognitionEnsemble Learning	—Unverified
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data	Mar 21, 2024	Emotion RecognitionEvent Detection	—Unverified
The Power of Properties: Uncovering the Influential Factors in Emotion Classification	Apr 11, 2024	Decision MakingEmotion Classification	—Unverified
The Pursuit of Empathy: Evaluating Small Language Models for PTSD Dialogue Support	May 21, 2025	Emotion Recognition	—Unverified
The Role of Phonetic Units in Speech Emotion Recognition	Aug 2, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition	Mar 25, 2024	Emotion RecognitionIn-Context Learning	—Unverified
The Super Emotion Dataset	May 21, 2025	Emotion ClassificationEmotion Recognition	—Unverified
The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data	Sep 22, 2020	BIG-bench Machine LearningEmotion Recognition	—Unverified
The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research	Dec 1, 2021	Emotion RecognitionSentiment Analysis	—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