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 1561–1570 of 2041 papers

Title	Date	Tasks	Status
How to read faces without looking at them	Apr 25, 2020	Emotion Recognition	—Unverified
A Wearable Social Interaction Aid for Children with Autism	Apr 19, 2020	Emotion RecognitionMemorization	—Unverified
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition	Apr 4, 2020	Brain Computer InterfaceClustering	—Unverified
On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers	Apr 1, 2020	Emotion Recognitionregression	CodeCode Available
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition	Mar 24, 2020	Emotion RecognitionTransfer Learning	—Unverified
Cross Lingual Cross Corpus Speech Emotion Recognition	Mar 18, 2020	Cross-corpusEmotion Recognition	—Unverified
EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle	Mar 14, 2020	Emotion RecognitionEmotion Recognition in Context	—Unverified
Emotion Recognition From Gait Analyses: Current Research and Future Directions	Mar 13, 2020	Emotion Recognition	—Unverified
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data	Mar 9, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data	Mar 5, 2020	Audio GenerationEmotion 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