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 801–810 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition Using Fusion of Audio and Video Features	Jun 25, 2019	Emotion RecognitionTransfer Learning	—Unverified
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems	Nov 16, 2021	Emotion RecognitionTask-Oriented Dialogue Systems	—Unverified
Empathetic Conversational Agents: Utilizing Neural and Physiological Signals for Enhanced Empathetic Interactions	Jan 14, 2025	Emotion Recognition	—Unverified
A Peek at Peak Emotion Recognition	May 19, 2022	Deep LearningEmotion Recognition	—Unverified
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models	May 1, 2022	ClassificationEmotion Classification	—Unverified
Empathy Through Multimodality in Conversational Interfaces	May 8, 2024	Emotional IntelligenceEmotion Recognition	—Unverified
A hierarchical approach with feature selection for emotion recognition from speech	May 1, 2012	ClassificationDimensionality Reduction	—Unverified
Empirical Interpretation of Speech Emotion Perception with Attention Based Model for Speech Emotion Recognition	Oct 28, 2020	Emotional IntelligenceEmotion Recognition	—Unverified
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition	Jun 30, 2023	Emotional IntelligenceEmotion Recognition	—Unverified
Emotion Recognition Using Convolutional Neural Networks	Apr 3, 2025	Emotion Recognitionregression	—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