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 1791–1800 of 2041 papers

Title	Date	Tasks	Status
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues	Jul 1, 2018	Common Sense ReasoningEmotion Recognition	—Unverified
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion	Jul 1, 2018	Emotion RecognitionMachine Translation	—Unverified
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling	Jul 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier	Jul 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph	Jul 1, 2018	Emotion RecognitionLanguage Modeling	—Unverified
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions	Jul 1, 2018	Emotion Recognition	—Unverified
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words	Jul 1, 2018	Emotion Recognition	—Unverified
It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems	Jun 29, 2018	AllEmotion Recognition	—Unverified
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding	Jun 29, 2018	Action RecognitionEmotion Recognition	—Unverified
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech	Jun 23, 2018	ClassificationEmotion Recognition	CodeCode Available

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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