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 1911–1920 of 2041 papers

Title	Date	Tasks	Status
Deep Factorization for Speech Signal	Jun 5, 2017	Emotion Recognition	—Unverified
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units	Jun 5, 2017	Emotion Recognition	—Unverified
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech	Jun 2, 2017	Emotion RecognitionMULTI-VIEW LEARNING	—Unverified
Spatial-Temporal Recurrent Neural Network for Emotion Recognition	May 12, 2017	EEGElectroencephalogram (EEG)	—Unverified
Word Affect Intensities	Apr 28, 2017	Emotion RecognitionText Generation	—Unverified
End-to-End Multimodal Emotion Recognition using Deep Neural Networks	Apr 27, 2017	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
Semi-supervised Bayesian Deep Multi-modal Emotion Recognition	Apr 25, 2017	Emotion RecognitionImputation	—Unverified
CNN based music emotion classification	Apr 19, 2017	ClassificationEmotion Classification	—Unverified
Improved Human Emotion Recognition Using Symmetry of Facial Key Points with Dihedral Group	Apr 14, 2017	Emotion Recognition	—Unverified
Spatiotemporal Networks for Video Emotion Recognition	Apr 3, 2017	ClassificationEmotion 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