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 1731–1740 of 2041 papers

Title	Date	Tasks	Status
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors	Nov 23, 2018	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
Towards Emotion Recognition: A Persistent Entropy Application	Nov 21, 2018	ClassificationEmotion Classification	—Unverified
PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship	Nov 21, 2018	Emotion RecognitionMulti-Task Learning	CodeCode Available
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks	Nov 19, 2018	Emotion RecognitionMULTI-VIEW LEARNING	—Unverified
Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition	Nov 19, 2018	ClassificationEEG	—Unverified
A Study of Language and Classifier-independent Feature Analysis for Vocal Emotion Recognition	Nov 14, 2018	Emotion Recognition	—Unverified
Angry or Climbing Stairs? Towards Physiological Emotion Recognition in the Wild	Nov 12, 2018	Emotion Recognition	—Unverified
MULTI-MODAL EMOTION RECOGNITION ON IEMOCAP WITH NEURAL NETWORKS.	Nov 12, 2018	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions	Nov 11, 2018	Emotion RecognitionGenerative Adversarial Network	—Unverified
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning	Nov 11, 2018	Emotion RecognitionSpeech 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