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

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Showing 1821–1830 of 2041 papers

Title	Date	Tasks	Status
Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for Neurohumanities	Jan 28, 2024	Arousal EstimationDominance Estimation	CodeCode Available
Multimodal Language Analysis with Recurrent Multistage Fusion	Aug 12, 2018	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
Synthetic Data Generation of Body Motion Data by Neural Gas Network for Emotion Recognition	Mar 11, 2025	DiversityEmotion Recognition	CodeCode Available
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech Processing	Jun 11, 2020	Emotion Recognitionspeech-recognition	CodeCode Available
Real-time emotion recognition for gaming using deep convolutional network features	Aug 16, 2014	Emotion Recognition	CodeCode Available
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network	Nov 20, 2019	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
Efficient Arabic emotion recognition using deep neural networks	Oct 31, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes	Jul 14, 2020	DescriptiveEmotion Recognition	CodeCode Available
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition	Apr 30, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Interpretable Multimodal Emotion Recognition using Hybrid Fusion of Speech and Image Data	Aug 25, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available

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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