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 491–500 of 2041 papers

Title	Date	Tasks	Status
DASB -- Discrete Audio and Speech Benchmark	Jun 20, 2024	BenchmarkingEmotion Recognition	—Unverified
A Multilingual Natural Stress Emotion Database	May 1, 2012	Emotion RecognitionSpeech Synthesis	—Unverified
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models	Jun 4, 2020	Data AugmentationEEG	—Unverified
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer	Nov 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Computational Emotion Analysis From Images: Recent Advances and Future Directions	Mar 19, 2021	Domain AdaptationEmotion Recognition	—Unverified
Compound Expression Recognition via Large Vision-Language Models	Mar 14, 2025	Emotion RecognitionZero-shot Generalization	—Unverified
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks	Apr 29, 2025	Contrastive LearningEEG	—Unverified
Deciphering Emotions in Children Storybooks: A Comparative Analysis of Multimodal LLMs in Educational Applications	Jun 22, 2025	Emotion Recognition	—Unverified
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning	Nov 25, 2024	Contrastive LearningCross-corpus	—Unverified
Developing emotion recognition for video conference software to support people with autism	Jan 26, 2021	Emotion 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