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 1431–1440 of 2041 papers

Title	Date	Tasks	Status	Hype
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition	Oct 28, 2020	Emotional IntelligenceEmotion Recognition	—Unverified	0
Generative Adversarial Networks in Human Emotion Synthesis:A Review	Oct 28, 2020	Emotion Recognition	—Unverified	0
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset	Oct 28, 2020	DecoderEmotion Recognition	CodeCode Available	1
Speech-Based Emotion Recognition using Neural Networks and Information Visualization	Oct 28, 2020	BIG-bench Machine LearningEmotion Classification	CodeCode Available	0
CopyPaste: An Augmentation Method for Speech Emotion Recognition	Oct 27, 2020	Data AugmentationEmotion Recognition	—Unverified	0
Hybrid Backpropagation Parallel Reservoir Networks	Oct 27, 2020	EEGElectroencephalogram (EEG)	—Unverified	0
Deformable Convolutional LSTM for Human Body Emotion Recognition	Oct 27, 2020	Emotion Recognition	CodeCode Available	0
Neural Architecture Search of SPD Manifold Networks	Oct 27, 2020	Emotion RecognitionNeural Architecture Search	CodeCode Available	1
Emotion recognition by fusing time synchronous and time asynchronous representations	Oct 27, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning	Oct 27, 2020	Emotion RecognitionRepresentation Learning	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