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 1111–1120 of 2041 papers

Title	Date	Tasks	Status
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis	Jun 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Push the Limit of Multi-modal Emotion Recognition by Prompting LLMs with Receptive-Field-Aware Attention Weighting	Nov 26, 2024	Emotion Recognition	—Unverified
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations	Mar 5, 2025	AllAutomatic Speech Recognition	—Unverified
Quality at the Tail of Machine Learning Inference	Dec 25, 2022	Autonomous DrivingBenchmarking	—Unverified
Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition	Jun 23, 2025	Emotion RecognitionFacial Emotion Recognition	—Unverified
Real-time Automatic Emotion Recognition from Body Gestures	Feb 20, 2014	Emotion Recognition	—Unverified
Real-time EEG-based Emotion Recognition using Discrete Wavelet Transforms on Full and Reduced Channel Signals	Oct 11, 2021	EEGElectroencephalogram (EEG)	—Unverified
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond	Jul 5, 2024	Deep LearningEmotional Intelligence	—Unverified
Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach	Apr 28, 2025	Emotion Recognition	—Unverified
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems	Nov 1, 2016	Dialogue ManagementEmotion Recognition	—Unverified

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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