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 751–760 of 2041 papers

Title	Date	Tasks	Status	Hype
Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals	Jun 5, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues	Jun 3, 2023	BinarizationEmotion Recognition	—Unverified	0
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations	Jun 2, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models	May 30, 2023	Emotion RecognitionSelf-Supervised Learning	—Unverified	0
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks	May 29, 2023	Emotion RecognitionSpeaker Recognition	—Unverified	0
Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal	May 27, 2023	EEGEmotion Recognition	—Unverified	0
ArPanEmo: An Open-Source Dataset for Fine-Grained Emotion Recognition in Arabic Online Content during COVID-19 Pandemic	May 27, 2023	Emotion Recognition	—Unverified	0
Automatic Emotion Experiencer Recognition	May 26, 2023	Emotion ClassificationEmotion Recognition	—Unverified	0
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition	May 25, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data	May 25, 2023	Emotion RecognitionSentiment Analysis	—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