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 341–350 of 2041 papers

Title	Date	Tasks	Status	Score
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography	Sep 11, 2019	EEGElectroencephalogram (EEG)	CodeCode Available	5
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake	Mar 31, 2024	Audio Deepfake DetectionDeepFake Detection	CodeCode Available	5
Group-level Emotion Recognition using Transfer Learning from Face Identification	Sep 6, 2017	Emotion RecognitionFace Identification	CodeCode Available	5
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond	Nov 6, 2018	Action LocalizationData Augmentation	CodeCode Available	5
Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model	May 26, 2025	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions	Jul 25, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
A speech corpus of Quechua Collao for automatic dimensional emotion recognition	Dec 24, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition	May 1, 2024	Active LearningEmotion Recognition	CodeCode Available	5
Graph-Enhanced Emotion Neural Decoding	Feb 16, 2023	Emotion RecognitionRepresentation Learning	CodeCode Available	5
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling	Feb 25, 2024	ChatbotDiversity	CodeCode Available	5

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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