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 1461–1470 of 2041 papers

Title	Date	Tasks	Status	Hype
Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese	Sep 23, 2020	Emotion Recognition	—Unverified	0
The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data	Sep 22, 2020	BIG-bench Machine LearningEmotion Recognition	—Unverified	0
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition	Sep 21, 2020	EEGEEG Emotion Recognition	—Unverified	0
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition	Sep 21, 2020	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
Optimizing Speech Emotion Recognition using Manta-Ray Based Feature Selection	Sep 18, 2020	ClassificationEmotion Recognition	—Unverified	0
Learning Emotional-Blinded Face Representations	Sep 18, 2020	AttributeEmotion Recognition	—Unverified	0
Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach	Sep 15, 2020	Emotion Recognition	—Unverified	0
Multi-modal embeddings using multi-task learning for emotion recognition	Sep 10, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition	Sep 7, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	0
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition	Sep 6, 2020	Emotion Recognitionfeature selection	—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