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 1871–1880 of 2041 papers

Title	Date	Tasks	Status
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis	Jan 16, 2024	DenoisingEmotional Speech Synthesis	—Unverified
EEG based Emotion Recognition: A Tutorial and Review	Mar 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
EEG based Emotion Recognition of Image Stimuli	Sep 11, 2019	EEGElectroencephalogram (EEG)	—Unverified
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning	Sep 2, 2022	Contrastive LearningEEG	—Unverified
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition	Aug 9, 2023	EEGEEG Emotion Recognition	—Unverified
EEG-based Multimodal Representation Learning for Emotion Recognition	Oct 29, 2024	EEGElectroencephalogram (EEG)	—Unverified
EEG emotion recognition using dynamical graph convolutional neural networks	Mar 21, 2018	EEGEEG Emotion Recognition	—Unverified
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition	Feb 7, 2021	EEGElectroencephalogram (EEG)	—Unverified
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification	Jul 20, 2024	EEGElectroencephalogram (EEG)	—Unverified
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters	Oct 19, 2021	EEGEeg Decoding	—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