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 1761–1770 of 2041 papers

Title	Date	Tasks	Status
Pretrained audio neural networks for Speech emotion recognition in Portuguese	Oct 26, 2022	Data AugmentationEmotion Recognition	CodeCode Available
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network	Feb 14, 2017	Emotion Recognition	CodeCode Available
Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning	Sep 18, 2019	BIG-bench Machine LearningEmotion Recognition	CodeCode Available
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition	Oct 29, 2023	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography	Sep 11, 2019	EEGElectroencephalogram (EEG)	CodeCode Available
Customising General Large Language Models for Specialised Emotion Recognition Tasks	Apr 14, 2024	Emotion RecognitionLanguage Modeling	CodeCode Available
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis	Jul 28, 2020	Emotion RecognitionSentiment Analysis	CodeCode Available
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition	Oct 24, 2019	Emotion RecognitionFacial Action Unit Detection	CodeCode Available
BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion Recognition	Jul 17, 2024	Emotion RecognitionPosition	CodeCode Available
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition	Jul 11, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available

Show:10 25 50

← PrevPage 177 of 205Next →

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