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 1571–1580 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks	Feb 29, 2020	Emotion Recognition	—Unverified
Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition	Feb 26, 2020	AttributeEmotion Recognition	CodeCode Available
Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks	Feb 20, 2020	Emotion RecognitionVideo Emotion Recognition	—Unverified
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models	Feb 20, 2020	Emotion RecognitionEvent Detection	—Unverified
Emotion Recognition for In-the-wild Videos	Feb 13, 2020	Emotion RecognitionGeneral Classification	—Unverified
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition	Feb 12, 2020	Emotion ClassificationEmotion Recognition	—Unverified
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos	Feb 12, 2020	Emotion RecognitionVideo Emotion Recognition	CodeCode Available
Emotion Recognition Using Speaker Cues	Feb 4, 2020	Emotion RecognitionQuantization	—Unverified
Speech Emotion Recognition using Support Vector Machine	Feb 3, 2020	ClassificationEmotion Recognition	—Unverified
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor	Jan 31, 2020	Emotion RecognitionFacial Expression 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