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 301–310 of 2041 papers

Title	Date	Tasks	Status	Hype
Multitask Emotion Recognition with Incomplete Labels	Feb 10, 2020	Action Unit DetectionArousal Estimation	CodeCode Available	1
Self-supervised ECG Representation Learning for Emotion Recognition	Feb 4, 2020	Emotion RecognitionMulti-Task Learning	CodeCode Available	1
PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion Recognition	Oct 20, 2019	Emotion RecognitionSentence	CodeCode Available	1
Self-supervised Learning for ECG-based Emotion Recognition	Oct 14, 2019	Emotion RecognitionSelf-Supervised Learning	CodeCode Available	1
BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories	Oct 9, 2019	Emotion RecognitionSentence	CodeCode Available	1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation	Aug 30, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available	1
Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants	Aug 9, 2019	Emotion RecognitionPrivacy Preserving	CodeCode Available	1
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks	Jul 18, 2019	EEGEEG Emotion Recognition	CodeCode Available	1
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts	Jun 4, 2019	Emotion Cause ExtractionEmotion-Cause Pair Extraction	CodeCode Available	1
HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition	Apr 9, 2019	Emotion Recognition	CodeCode Available	1

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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