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 1191–1200 of 2041 papers

Title	Date	Tasks	Status
Siamese Generative Adversarial Privatizer for Biometric Data	Apr 23, 2018	Emotion Recognition	—Unverified
Silent Speech and Emotion Recognition from Vocal Tract Shape Dynamics in Real-Time MRI	Jun 16, 2021	Emotion RecognitionSentence	—Unverified
SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation	May 4, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy	Aug 12, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification	Oct 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets	Jun 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors	Nov 8, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues	Jul 1, 2018	Common Sense ReasoningEmotion Recognition	—Unverified
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network	Oct 24, 2021	Emotion RecognitionObject	—Unverified
Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks	Sep 24, 2022	Emotion Recognition	—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