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 761–770 of 2041 papers

Title	Date	Tasks	Status	Hype
Transfer Learning for Personality Perception via Speech Emotion Recognition	May 25, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search	May 23, 2023	Emotion RecognitionNeural Architecture Search	CodeCode Available	1
Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition	May 23, 2023	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available	0
Disentangled Variational Autoencoder for Emotion Recognition in Conversations	May 23, 2023	Emotion RecognitionResponse Generation	CodeCode Available	1
Learning Emotion Representations from Verbal and Nonverbal Communication	May 22, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition	May 21, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Self-supervised representations in speech-based depression detection	May 20, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition	May 18, 2023	EEGEEG Emotion Recognition	—Unverified	0
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation	May 15, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition	May 12, 2023	EEGElectroencephalogram (EEG)	—Unverified	0

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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