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 701–710 of 2041 papers

Title	Date	Tasks	Status	Hype
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations	Aug 6, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques	Aug 4, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition	Aug 4, 2023	Cross-corpusDomain Adaptation	CodeCode Available	1
Contextual Emotion Recognition Using Transformer-Based Models	Aug 2, 2023	Emotion RecognitionLanguage Modelling	CodeCode Available	0
Using Scene and Semantic Features for Multi-modal Emotion Recognition	Aug 1, 2023	Emotion Recognition	—Unverified	0
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition	Jul 28, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1
HTNet for micro-expression recognition	Jul 27, 2023	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	1
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion	Jul 25, 2023	Emotional IntelligenceEmotion Recognition	—Unverified	0
Self-Supervised Learning for Audio-Based Emotion Recognition	Jul 23, 2023	Emotion RecognitionMarketing	—Unverified	0
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition	Jul 23, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	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