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 821–830 of 2041 papers

Title	Date	Tasks	Status	Hype
CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate	Mar 10, 2023	Emotion RecognitionPosition	—Unverified	0
A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces	Mar 8, 2023	Emotion Recognition	—Unverified	0
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis	Mar 3, 2023	Emotion RecognitionKnowledge Distillation	—Unverified	0
DWFormer: Dynamic Window transFormer for Speech Emotion Recognition	Mar 3, 2023	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing	Feb 27, 2023	Alzheimer's Disease DetectionEmotion Recognition	CodeCode Available	1
Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition	Feb 27, 2023	Emotion RecognitionMultimodal Emotion Recognition	—Unverified	0
A low latency attention module for streaming self-supervised speech representation learning	Feb 27, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics	Feb 26, 2023	Emotion RecognitionMusic Emotion Recognition	CodeCode Available	0
Partial Label Learning for Emotion Recognition from EEG	Feb 25, 2023	EEGElectroencephalogram (EEG)	CodeCode Available	1
Ensemble knowledge distillation of self-supervised speech models	Feb 24, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—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