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 1421–1430 of 2041 papers

Title	Date	Tasks	Status
Multi-Task Self-Supervised Pre-Training for Music Classification	Feb 5, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation	Feb 3, 2021	Data AugmentationEmotion Recognition	—Unverified
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition	Feb 3, 2021	Active LearningEmotion Recognition	—Unverified
Neural Network architectures to classify emotions in Indian Classical Music	Feb 1, 2021	Emotion RecognitionMusic Emotion Recognition	—Unverified
FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition	Feb 1, 2021	Active LearningBrain Computer Interface	CodeCode Available
Developing emotion recognition for video conference software to support people with autism	Jan 26, 2021	Emotion Recognition	—Unverified
Expression Recognition Analysis in the Wild	Jan 22, 2021	Emotion RecognitionFacial Expression Recognition	—Unverified
Analysis of Basic Emotions in Texts Based on BERT Vector Representation	Jan 21, 2021	Emotion Recognition	—Unverified
4D Attention-based Neural Network for EEG Emotion Recognition	Jan 14, 2021	Brain Computer InterfaceEEG	—Unverified
Fast Facial Landmark Detection and Applications: A Survey	Jan 12, 2021	Emotion RecognitionFace Reenactment	—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