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 771–780 of 2041 papers

Title	Date	Tasks	Status
Emotion recognition techniques with rule based and machine learning approaches	Feb 28, 2021	BIG-bench Machine LearningCultural Vocal Bursts Intensity Prediction	—Unverified
Emotion Recognition under Consideration of the Emotion Component Process Model	Jul 27, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features	May 19, 2020	Emotion RecognitionHeart Rate Variability	—Unverified
Emotion Recognition Using Convolutional Neural Networks	Apr 3, 2025	Emotion Recognitionregression	—Unverified
Emotion Recognition Using Fusion of Audio and Video Features	Jun 25, 2019	Emotion RecognitionTransfer Learning	—Unverified
Emotion Recognition using Machine Learning and ECG signals	Mar 16, 2022	BIG-bench Machine LearningEmotion Recognition	—Unverified
EEG based Emotion Recognition: A Tutorial and Review	Mar 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
Emotion Recognition Using Speaker Cues	Feb 4, 2020	Emotion RecognitionQuantization	—Unverified
Bias and Fairness on Multimodal Emotion Detection Algorithms	May 11, 2022	Emotion RecognitionFairness	—Unverified
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis	Jan 16, 2024	DenoisingEmotional Speech Synthesis	—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