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 881–890 of 2041 papers

Title	Date	Tasks	Status	Hype
Deep Emotion Recognition in Textual Conversations: A Survey	Nov 16, 2022	BenchmarkingEmotion Recognition	CodeCode Available	0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer	Nov 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition	Nov 16, 2022	EEGElectroencephalogram (EEG)	—Unverified	0
Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches	Nov 15, 2022	Data AugmentationEmotion Recognition	CodeCode Available	0
Sentiment recognition of Italian elderly through domain adaptation on cross-corpus speech dataset	Nov 14, 2022	Cross-corpusDomain Adaptation	—Unverified	0
YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations	Nov 14, 2022	Emotion RecognitionMusic Generation	CodeCode Available	1
Describing emotions with acoustic property prompts for speech emotion recognition	Nov 14, 2022	Emotion RecognitionRetrieval	—Unverified	0
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement	Nov 12, 2022	Data AugmentationEmotion Recognition	—Unverified	0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations	Nov 11, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection	Nov 9, 2022	Emotion RecognitionFacial Emotion Recognition	—Unverified	0

Show:10 25 50

← PrevPage 89 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