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 1471–1480 of 2041 papers

Title	Date	Tasks	Status
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis	Nov 5, 2020	Emotion Recognitionimage-classification	CodeCode Available
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?	Nov 3, 2020	BIG-bench Machine LearningClassification	—Unverified
Robust Latent Representations via Cross-Modal Translation and Alignment	Nov 3, 2020	Emotion RecognitionTranslation	—Unverified
Identifying Worry in Twitter: Beyond Emotion Analysis	Nov 1, 2020	Decision MakingEmotion Recognition	—Unverified
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic	Nov 1, 2020	Emotion RecognitionTime Series	—Unverified
Analysis of Resource-efficient Predictive Models for Natural Language Processing	Nov 1, 2020	BIG-bench Machine LearningEmotion Recognition	—Unverified
Efficient Arabic emotion recognition using deep neural networks	Oct 31, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition	Oct 30, 2020	EEGElectroencephalogram (EEG)	—Unverified
Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation	Oct 30, 2020	Action RecognitionEmotion Interpretation	—Unverified
Generative Adversarial Networks in Human Emotion Synthesis:A Review	Oct 28, 2020	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