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 721–730 of 2041 papers

Title	Date	Tasks	Status
Emotion Dynamics Modeling via BERT	Apr 15, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Emotion Embeddings x2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets	Aug 15, 2023	DiversityEmotion Recognition	—Unverified
Bimodal Connection Attention Fusion for Speech Emotion Recognition	Mar 8, 2025	DecoderEmotion Recognition	—Unverified
EEG-based Multimodal Representation Learning for Emotion Recognition	Oct 29, 2024	EEGElectroencephalogram (EEG)	—Unverified
Emotion Generation and Recognition: A StarGAN Approach	Oct 12, 2019	4kEmotion Recognition	—Unverified
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis	Nov 28, 2019	Emotion RecognitionSentiment Analysis	—Unverified
Climate and Weather: Inspecting Depression Detection via Emotion Recognition	Apr 29, 2022	Depression DetectionEmotion Recognition	—Unverified
Emotion in Code-switching Texts: Corpus Construction and Analysis	Jul 1, 2015	Emotion Recognition	—Unverified
An Efficient End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition	Sep 20, 2022	Emotion Recognition	—Unverified
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study	Aug 28, 2023	ClassificationEmotion Classification	—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