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 1611–1620 of 2041 papers

Title	Date	Tasks	Status	Hype
Domain adversarial learning for emotion recognition	Oct 24, 2019	Emotion Recognition	—Unverified	0
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition	Oct 24, 2019	Emotion RecognitionRepresentation Learning	—Unverified	0
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition	Oct 24, 2019	Emotion RecognitionFacial Action Unit Detection	CodeCode Available	0
Conversational Emotion Analysis via Attention Mechanisms	Oct 24, 2019	Emotion Recognition	—Unverified	0
Emotion recognition with 4kresolution database	Oct 24, 2019	Emotion Recognition	—Unverified	0
Expression Analysis Based on Face Regions in Read-world Conditions	Oct 23, 2019	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
Speech Emotion Recognition via Contrastive Loss under Siamese Networks	Oct 23, 2019	Emotion Recognitionfeature selection	—Unverified	0
Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening	Oct 22, 2019	EEGElectroencephalogram (EEG)	CodeCode Available	0
Speech Emotion Recognition with Dual-Sequence LSTM Architecture	Oct 20, 2019	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion Recognition	Oct 20, 2019	Emotion RecognitionSentence	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