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 1821–1830 of 2041 papers

Title	Date	Tasks	Status
3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children With Autism	Jun 1, 2018	Emotion Recognition	—Unverified
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets	Jun 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors	May 31, 2018	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
Context-aware Cascade Attention-based RNN for Video Emotion Recognition	May 30, 2018	DecoderEmotion Classification	—Unverified
Curriculum Learning for Speech Emotion Recognition from Crowdsourced Labels	May 25, 2018	Emotion RecognitionMulti-class Classification	—Unverified
Meta Transfer Learning for Facial Emotion Recognition	May 25, 2018	Deep LearningEmotion Recognition	—Unverified
ASR-based Features for Emotion Recognition: A Transfer Learning Approach	May 23, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data	May 17, 2018	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Transformer for Emotion Recognition	May 3, 2018	Emotion Recognition	CodeCode Available
Dimensional emotion recognition using visual and textual cues	May 3, 2018	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