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 1421–1430 of 2041 papers

Title	Date	Tasks	Status	Hype
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?	Nov 3, 2020	BIG-bench Machine LearningClassification	—Unverified	0
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic	Nov 1, 2020	Emotion RecognitionTime Series	—Unverified	0
Identifying Worry in Twitter: Beyond Emotion Analysis	Nov 1, 2020	Decision MakingEmotion Recognition	—Unverified	0
Analysis of Resource-efficient Predictive Models for Natural Language Processing	Nov 1, 2020	BIG-bench Machine LearningEmotion Recognition	—Unverified	0
Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations	Nov 1, 2020	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1
Efficient Arabic emotion recognition using deep neural networks	Oct 31, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	0
Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation	Oct 30, 2020	Action RecognitionEmotion Interpretation	—Unverified	0
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition	Oct 30, 2020	EEGElectroencephalogram (EEG)	—Unverified	0
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss	Oct 30, 2020	Emotion RecognitionWord Embeddings	CodeCode Available	1
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition	Oct 28, 2020	Emotion RecognitionMultimodal Emotion 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