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 1291–1300 of 2041 papers

Title	Date	Tasks	Status
Think out Loud: Emotion Deducing Explanation in Dialogues	Jun 7, 2024	Common Sense ReasoningEmotion Cause Extraction	—Unverified
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN	Jun 1, 2019	Emotion ClassificationEmotion Recognition	—Unverified
TNTC: two-stream network with transformer-based complementarity for gait-based emotion recognition	Oct 26, 2021	Emotion Recognition	—Unverified
Tollywood Emotions: Annotation of Valence-Arousal in Telugu Song Lyrics	Mar 16, 2023	Emotion RecognitionMusic Emotion Recognition	—Unverified
Tools for Digital Humanities: Enabling Access to the Old Occitan Romance of Flamenca	Jun 1, 2015	Emotion RecognitionWord Alignment	—Unverified
Topic-Based Chinese Message Sentiment Analysis: A Multilayered Analysis System	Jul 1, 2015	Emotion RecognitionSentiment Analysis	—Unverified
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition	Mar 14, 2022	Arousal EstimationDominance Estimation	—Unverified
Toward a Dialogue System Using a Large Language Model to Recognize User Emotions with a Camera	Aug 15, 2024	AI AgentChatbot	—Unverified
Toward end-to-end interpretable convolutional neural networks for waveform signals	May 3, 2024	BenchmarkingEmotion Recognition	—Unverified
Towards a Common Speech Analysis Engine	Mar 1, 2022	Emotion RecognitionLanguage Identification	—Unverified

Show:10 25 50

← PrevPage 130 of 205Next →

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