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 1371–1380 of 2041 papers

Title	Date	Tasks	Status
Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis	Mar 27, 2025	Emotion RecognitionSentiment Analysis	—Unverified
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification	Oct 1, 2015	Document ClassificationDomain Adaptation	—Unverified
Hybrid Models for Facial Emotion Recognition in Children	Aug 24, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
Hybrid Mutimodal Fusion for Dimensional Emotion Recognition	Oct 16, 2021	Emotion Recognition	—Unverified
Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG Signal Analysis	Nov 19, 2024	Deep LearningEEG	—Unverified
HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition	Jun 3, 2025	Emotion RecognitionRepresentation Learning	—Unverified
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction	Jan 12, 2020	Emotion ClassificationEmotion Recognition	—Unverified
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter	Jun 1, 2016	Emotion RecognitionGeneral Classification	—Unverified
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data	Aug 24, 2022	Emotion RecognitionOptical Flow Estimation	—Unverified
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection	Oct 1, 2018	Emotion RecognitionEmotion Recognition in Conversation	—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