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 1511–1520 of 2041 papers

Title	Date	Tasks	Status
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition	Sep 6, 2020	Emotion Recognitionfeature selection	—Unverified
Fine-grained Early Frequency Attention for Deep Speaker Representation Learning	Sep 3, 2020	Deep LearningEmotion Recognition	—Unverified
Convolutional Speech Recognition with Pitch and Voice Quality Features	Sep 2, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Suspect AI: Vibraimage, Emotion Recognition Technology, and Algorithmic Opacity	Sep 1, 2020	Emotion Recognition	—Unverified
Introducing Representations of Facial Affect in Automated Multimodal Deception Detection	Aug 31, 2020	Deception DetectionEmotion Recognition	—Unverified
Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data	Aug 24, 2020	BIG-bench Machine LearningEmotion Recognition	—Unverified
EmoGraph: Capturing Emotion Correlations using Graph Networks	Aug 21, 2020	ClassificationEmotion Classification	—Unverified
Emotion Carrier Recognition from Personal Narratives	Aug 17, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!	Aug 17, 2020	Emotion RecognitionFacial Emotion Recognition	—Unverified
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition	Aug 6, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—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