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 1481–1490 of 2041 papers

Title	Date	Tasks	Status	Hype
Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!	Aug 17, 2020	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition	Aug 15, 2020	Emotion RecognitionMultimodal Deep Learning	CodeCode Available	1
SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!	Aug 9, 2020	Emotion Recognition	CodeCode Available	1
Speech Driven Talking Face Generation from a Single Image and an Emotion Condition	Aug 8, 2020	Emotion RecognitionFace Generation	CodeCode Available	1
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition	Aug 6, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Dynamic Emotion Modeling with Learnable Graphs and Graph Inception Network	Aug 6, 2020	Emotion Recognitiongraph construction	CodeCode Available	1
Compact Graph Architecture for Speech Emotion Recognition	Aug 5, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1
The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems	Jul 29, 2020	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis	Jul 28, 2020	ClassificationEmotion Recognition	CodeCode Available	0
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis	Jul 28, 2020	Emotion RecognitionSentiment Analysis	CodeCode Available	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