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 721–730 of 2041 papers

Title	Date	Tasks	Status	Hype
SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images	Jul 6, 2023	Emotion Recognition	—Unverified	0
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition	Jul 6, 2023	EEGEEG Emotion Recognition	—Unverified	0
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition	Jul 6, 2023	Emotion RecognitionEnsemble Learning	—Unverified	0
Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM	Jul 4, 2023	Emotion Recognitionfeature selection	—Unverified	0
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog	Jul 2, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party Conversations	Jul 1, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition	Jun 30, 2023	Emotional IntelligenceEmotion Recognition	—Unverified	0
EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech	Jun 28, 2023	Emotion RecognitionSpeech Synthesis	CodeCode Available	1
Emotion Analysis of Tweets Banning Education in Afghanistan	Jun 28, 2023	BenchmarkingEmotion Classification	—Unverified	0
Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations	Jun 27, 2023	Emotion Recognition	CodeCode Available	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