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

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Showing 1851–1860 of 2041 papers

Title	Date	Tasks	Status
Domain Adversarial for Acoustic Emotion Recognition	Apr 20, 2018	AttributeEmotion Recognition	—Unverified
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning	Apr 16, 2018	Deep LearningEmotion Recognition	CodeCode Available
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks	Apr 6, 2018	Data AugmentationEmotion Recognition	—Unverified
Reusing Neural Speech Representations for Auditory Emotion Recognition	Mar 30, 2018	Emotion RecognitionGeneral Classification	—Unverified
EEG emotion recognition using dynamical graph convolutional neural networks	Mar 21, 2018	EEGEEG Emotion Recognition	—Unverified
Speech Emotion Recognition Considering Local Dynamic Features	Mar 21, 2018	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Deep learning for affective computing: text-based emotion recognition in decision support	Mar 16, 2018	Decision MakingEmotion Recognition	—Unverified
Cross-lingual and Multilingual Speech Emotion Recognition on English and French	Mar 1, 2018	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Deep factorization for speech signal	Feb 27, 2018	Emotion RecognitionSpeaker Recognition	—Unverified
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets	Feb 26, 2018	Emotion RecognitionGeneral Classification	—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