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 461–470 of 2041 papers

Title	Date	Tasks	Status
Construction and Annotation of a French Folkstale Corpus	May 1, 2014	Emotion RecognitionInformation Retrieval	—Unverified
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers	Jun 23, 2023	Emotion ClassificationEmotion Recognition	—Unverified
A Pre-trained Audio-Visual Transformer for Emotion Recognition	Jan 23, 2022	Emotion ClassificationEmotion Recognition	—Unverified
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition	Sep 9, 2024	Computational EfficiencyEmotion Recognition	—Unverified
Cross Lingual Cross Corpus Speech Emotion Recognition	Mar 18, 2020	Cross-corpusEmotion Recognition	—Unverified
A Survey on Paralinguistics in Tamil Speech Processing	Apr 1, 2021	Emotion RecognitionSpeaker Identification	—Unverified
A Survey on Physiological Signal Based Emotion Recognition	May 20, 2022	Emotion RecognitionSurvey	—Unverified
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis	Oct 25, 2018	EEGElectroencephalogram (EEG)	—Unverified
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition	Jan 25, 2025	cross-modal alignmentEmotion Classification	—Unverified
ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition	May 15, 2020	Emotion RecognitionPacket Loss Concealment	—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