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 201–210 of 2041 papers

Title	Date	Tasks	Status	Hype
How Deep Neural Networks Can Improve Emotion Recognition on Video Data	Feb 24, 2016	Emotion Recognition	CodeCode Available	1
Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset	Oct 9, 2021	Deep LearningEmotion Recognition	CodeCode Available	1
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals	Oct 11, 2023	EEGElectroencephalogram (EEG)	CodeCode Available	1
IdentiFace : A VGG Based Multimodal Facial Biometric System	Jan 2, 2024	Decision MakingEmotion Recognition	CodeCode Available	1
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search	May 23, 2023	Emotion RecognitionNeural Architecture Search	CodeCode Available	1
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study	Oct 7, 2022	Cross-corpusEmotion Recognition	CodeCode Available	1
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer	Dec 21, 2021	Contrastive LearningDecoder	CodeCode Available	1
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks	Jul 5, 2024	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition	Aug 15, 2020	Emotion RecognitionMultimodal Deep Learning	CodeCode Available	1
A vector quantized masked autoencoder for speech emotion recognition	Apr 21, 2023	Emotion RecognitionSelf-Supervised 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