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 1121–1130 of 2041 papers

Title	Date	Tasks	Status
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition	Jul 11, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition	Jul 7, 2022	Cross-corpusDeep Reinforcement Learning	—Unverified
A cross-corpus study on speech emotion recognition	Jul 5, 2022	Cross-corpusEmotion Recognition	—Unverified
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network	Jul 4, 2022	Efficient Neural NetworkEmotion Recognition	—Unverified
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition	Jul 3, 2022	Emotion RecognitionGraph Neural Network	—Unverified
Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets	Jul 2, 2022	Emotion RecognitionSentiment Analysis	—Unverified
Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data	Jul 1, 2022	Emotion Recognitionregression	—Unverified
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation	Jul 1, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning	Jun 27, 2022	Emotion RecognitionPhoneme Recognition	—Unverified
Emotion recognition in the times of COVID19: Coping with face masks	Jun 26, 2022	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