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 971–980 of 2041 papers

Title	Date	Tasks	Status	Hype
MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG signals from wireless wearable low-cost off-the-shelf Devices	Jul 14, 2022	Emotion RecognitionPhotoplethysmography (PPG)	—Unverified	0
Emotion Recognition in Conversation using Probabilistic Soft Logic	Jul 14, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Semi-supervised cross-lingual speech emotion recognition	Jul 14, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition	Jul 12, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
EmoSens: Emotion Recognition based on Sensor data analysis using LightGBM	Jul 12, 2022	Emotion Recognition	—Unverified	0
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices	Jul 12, 2022	Emotion RecognitionKeyword Spotting	CodeCode Available	0
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition	Jul 11, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition	Jul 7, 2022	Cross-corpusDeep Reinforcement Learning	—Unverified	0
GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition	Jul 6, 2022	Emotion ClassificationEmotion Recognition	CodeCode Available	1
A cross-corpus study on speech emotion recognition	Jul 5, 2022	Cross-corpusEmotion Recognition	—Unverified	0

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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