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
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition	Jan 16, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Speech Emotion Recognition Based on Multi-feature and Multi-lingual Fusion	Jan 16, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Visually Guided Self Supervised Learning of Speech Representations	Jan 13, 2020	Emotion RecognitionRepresentation Learning	—Unverified
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction	Jan 12, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Facial Emotions Recognition using Convolutional Neural Net	Jan 6, 2020	Emotion RecognitionFace Detection	—Unverified
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends	Jan 2, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Ensemble emotion recognizing with multiple modal physiological signals	Jan 1, 2020	ClassificationEEG	—Unverified
Learning Transferable Features for Speech Emotion Recognition	Dec 23, 2019	Domain AdaptationEmotional Intelligence	—Unverified
Emotion Recognition from Speech	Dec 22, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available
Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress	Dec 22, 2019	Emotion RecognitionSystematic Literature Review	—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