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 1341–1350 of 2041 papers

Title	Date	Tasks	Status	Hype
EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition	Mar 10, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1
Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models	Mar 10, 2021	Emotion Recognition	CodeCode Available	0
A Discriminative Vectorial Framework for Multi-modal Feature Representation	Mar 9, 2021	Audio Emotion RecognitionData Visualization	—Unverified	0
Analyzing the Influence of Dataset Composition for Emotion Recognition	Mar 5, 2021	Emotion RecognitionMultimodal Emotion Recognition	—Unverified	0
An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions	Mar 5, 2021	Emotion Recognition	—Unverified	0
Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games	Mar 5, 2021	EEGElectroencephalogram (EEG)	—Unverified	0
Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing	Mar 4, 2021	Emotion Recognition	CodeCode Available	1
Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition	Mar 2, 2021	Emotion RecognitionSentence	CodeCode Available	1
Investigations on Audiovisual Emotion Recognition in Noisy Conditions	Mar 2, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Computationally Efficient Wasserstein Loss for Structured Labels	Mar 1, 2021	Age EstimationEmotion Recognition	—Unverified	0

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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