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 1221–1230 of 2041 papers

Title	Date	Tasks	Status
Speech Emotion Recognition using Self-Supervised Features	Feb 7, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
LEAPMood: Light and Efficient Architecture to Predict Mood with Genetic Algorithm driven Hyperparameter Tuning	Feb 5, 2022	Emotion RecognitionPrediction	—Unverified
Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors	Feb 2, 2022	Decision MakingEmotion Recognition	—Unverified
Speaker Normalization for Self-supervised Speech Emotion Recognition	Feb 2, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data	Jan 31, 2022	Emotion RecognitionEvent Detection	CodeCode Available
A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation	Jan 30, 2022	Emotion ClassificationEmotion Recognition	—Unverified
"That's so cute!": The CARE Dataset for Affective Response Detection	Jan 28, 2022	Emotion Recognition	—Unverified
Automated Feature Extraction on AsMap for Emotion Classification using EEG	Jan 28, 2022	Automated Feature EngineeringClassification	—Unverified
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition	Jan 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices	Jan 24, 2022	Emotion Recognition	CodeCode Available

Show:10 25 50

← PrevPage 123 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