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 1551–1560 of 2041 papers

Title	Date	Tasks	Status
Atom Search Optimization with Simulated Annealing -- a Hybrid Metaheuristic Approach for Feature Selection	May 10, 2020	Emotion RecognitionFacial Emotion Recognition	—Unverified
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?	May 4, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
An Event-comment Social Media Corpus for Implicit Emotion Analysis	May 1, 2020	ClassificationEmotion Classification	—Unverified
An Algerian Corpus and an Annotation Platform for Opinion and Emotion Analysis	May 1, 2020	Author ProfilingEmotion Recognition	—Unverified
IIIT-H TEMD Semi-Natural Emotional Speech Database from Professional Actors and Non-Actors	May 1, 2020	Emotion Recognition	—Unverified
Improving Sentiment Analysis with Biofeedback Data	May 1, 2020	Emotion RecognitionSentence	—Unverified
Speech-Emotion Detection in an Indonesian Movie	May 1, 2020	Emotion RecognitionGeneral Classification	—Unverified
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition	Apr 28, 2020	Emotion Recognition	—Unverified
Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training	Apr 27, 2020	Electromyography (EMG)Emotion Recognition	—Unverified
A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles	Apr 26, 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