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 951–960 of 2041 papers

Title	Date	Tasks	Status
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data	May 25, 2023	Emotion RecognitionSentiment Analysis	—Unverified
Transfer Learning for Personality Perception via Speech Emotion Recognition	May 25, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition	May 23, 2023	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition	May 21, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Self-supervised representations in speech-based depression detection	May 20, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition	May 18, 2023	EEGEEG Emotion Recognition	—Unverified
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation	May 15, 2023	Emotion ClassificationEmotion Recognition	—Unverified
A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition	May 12, 2023	EEGElectroencephalogram (EEG)	—Unverified
Versatile audio-visual learning for emotion recognition	May 12, 2023	Arousal EstimationAttribute	—Unverified
Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network	May 11, 2023	Emotion 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