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 1131–1140 of 2041 papers

Title	Date	Tasks	Status	Hype
Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition	Dec 23, 2021	ClassificationEEG	—Unverified	0
Your Face Mirrors Your Deepest Beliefs-Predicting Personality and Morals through Facial Emotion Recognition	Dec 23, 2021	Emotion RecognitionFacial Emotion Recognition	—Unverified	0
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation	Dec 23, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Hybrid Curriculum Learning for Emotion Recognition in Conversation	Dec 22, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified	0
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer	Dec 21, 2021	Contrastive LearningDecoder	CodeCode Available	1
Progressive Graph Convolution Network for EEG Emotion Recognition	Dec 14, 2021	EEGEEG Emotion Recognition	—Unverified	0
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations	Dec 13, 2021	Emotion RecognitionNatural Language Understanding	—Unverified	0
ValueNet: A New Dataset for Human Value Driven Dialogue System	Dec 12, 2021	Dialogue GenerationEmotion Recognition	—Unverified	0
The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)	Dec 10, 2021	Cultural Vocal Bursts Intensity PredictionEmotion Classification	CodeCode Available	0
LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences	Dec 3, 2021	Efficient Neural NetworkEmotion Recognition	—Unverified	0

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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