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 1301–1310 of 2041 papers

Title	Date	Tasks	Status	Hype
Deep scattering network for speech emotion recognition	May 11, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition	May 9, 2021	Emotion RecognitionSubject Transfer	—Unverified	0
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study	May 8, 2021	Action Unit DetectionAttribute	—Unverified	0
Facial Emotion Recognition: State of the Art Performance on FER2013	May 8, 2021	Computational EfficiencyEmotion Recognition	CodeCode Available	1
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora	May 5, 2021	ClusteringDecision Making	—Unverified	0
Technical Report for Valence-Arousal Estimation on Affwild2 Dataset	May 4, 2021	Arousal EstimationEmotion Recognition	CodeCode Available	1
Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers	May 1, 2021	BIG-bench Machine LearningEmotion Recognition	CodeCode Available	0
Facial Emotion Recognition Using Transfer Learning in the Deep CNN	Apr 27, 2021	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	1
Weakly-supervised Multi-task Learning for Multimodal Affect Recognition	Apr 23, 2021	Emotion RecognitionMulti-Task Learning	—Unverified	0
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech	Apr 23, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1

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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