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

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Showing 811–820 of 2041 papers

Title	Date	Tasks	Status
A Peek at Peak Emotion Recognition	May 19, 2022	Deep LearningEmotion Recognition	—Unverified
A hierarchical approach with feature selection for emotion recognition from speech	May 1, 2012	ClassificationDimensionality Reduction	—Unverified
Emotion Recognition Using Convolutional Neural Networks	Apr 3, 2025	Emotion Recognitionregression	—Unverified
End-to-End Continuous Speech Emotion Recognition in Real-life Customer Service Call Center Conversations	Oct 2, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training	Jun 26, 2019	Emotional Speech SynthesisEmotion Recognition	—Unverified
End-to-end facial and physiological model for Affective Computing and applications	Dec 10, 2019	Arousal EstimationDeep Learning	—Unverified
Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features	May 19, 2020	Emotion RecognitionHeart Rate Variability	—Unverified
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review	Sep 9, 2024	EEGElectroencephalogram (EEG)	—Unverified
Emotion Recognition under Consideration of the Emotion Component Process Model	Jul 27, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Emotion recognition techniques with rule based and machine learning approaches	Feb 28, 2021	BIG-bench Machine LearningCultural Vocal Bursts Intensity Prediction	—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