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 131–140 of 2041 papers

Title	Date	Tasks	Status	Hype
Speech Emotion Diarization: Which Emotion Appears When?	Jun 22, 2023	Emotion Recognitionspeaker-diarization	CodeCode Available	1
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression	Jun 11, 2023	AttributeEmotion Recognition	CodeCode Available	1
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning	Jun 10, 2023	Activity RecognitionClassification	CodeCode Available	1
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations	Jun 2, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
Disentangled Variational Autoencoder for Emotion Recognition in Conversations	May 23, 2023	Emotion RecognitionResponse Generation	CodeCode Available	1
Enhancing Speech Emotion Recognition Through Differentiable Architecture Search	May 23, 2023	Emotion RecognitionNeural Architecture Search	CodeCode Available	1
Learning Emotion Representations from Verbal and Nonverbal Communication	May 22, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation	May 5, 2023	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available	1
How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning	May 4, 2023	ARCCausal Discovery	CodeCode Available	1
Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention	Apr 29, 2023	Dialogue Act ClassificationDialogue Understanding	CodeCode Available	1

Show:10 25 50

← PrevPage 14 of 205Next →

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