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 591–600 of 2041 papers

Title	Date	Tasks	Status	Hype
SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults	Nov 24, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Dialogue Quality and Emotion Annotations for Customer Support Conversations	Nov 23, 2023	BenchmarkingDiversity	CodeCode Available	0
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition	Nov 22, 2023	Cross-corpusEmotion Recognition	—Unverified	0
Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces	Nov 20, 2023	Emotion Recognition	—Unverified	0
Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks	Nov 20, 2023	Age ClassificationAge Estimation	CodeCode Available	0
Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment Analysis	Nov 19, 2023	Emotion RecognitionSentiment Analysis	—Unverified	0
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition	Nov 18, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots	Nov 18, 2023	ChatbotEmotion Recognition	—Unverified	0
Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning	Nov 17, 2023	Emotion Recognition	—Unverified	0
Improving Unimodal Inference with Multimodal Transformers	Nov 16, 2023	Emotion RecognitionGesture Recognition	—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