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 1531–1540 of 2041 papers

Title	Date	Tasks	Status
Low Rank Fusion based Transformers for Multimodal Sequences	Jul 4, 2020	Emotion Recognition	—Unverified
Shallow over Deep Neural Networks: A empirical analysis for human emotion classification using audio data	Jul 3, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Systematic Evaluation of a Framework for Unsupervised Emotion Recognition for Narrative Text	Jul 1, 2020	Emotion Recognition	—Unverified
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis	Jul 1, 2020	Emotion RecognitionMulti-Task Learning	—Unverified
TRANSFER :- DEEP INDUCTIVE NETWORK FOR FACIAL EMOTION RECOGNITION	Jul 1, 2020	Emotion RecognitionFacial Emotion Recognition	CodeCode Available
Private Speech Classification with Secure Multiparty Computation	Jul 1, 2020	Audio ClassificationAudio Signal Processing	—Unverified
Towards Emotion-aided Multi-modal Dialogue Act Classification	Jul 1, 2020	ClassificationDialogue Act Classification	—Unverified
Contextualized Emotion Recognition in Conversation as Sequence Tagging	Jul 1, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Meta Transfer Learning for Emotion Recognition	Jun 23, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Manipulating emotions for ground truth emotion analysis	Jun 16, 2020	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