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 1201–1210 of 2041 papers

Title	Date	Tasks	Status
Temporal aggregation of audio-visual modalities for emotion recognition	Jul 8, 2020	Emotion Recognition	—Unverified
Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition	Dec 23, 2021	ClassificationEEG	—Unverified
TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification	Jan 13, 2024	Data IntegrationEmotion Recognition	—Unverified
Temporal Aware Mixed Attention-based Convolution and Transformer Network (MACTN) for EEG Emotion Recognition	May 18, 2023	EEGEEG Emotion Recognition	—Unverified
Temporal Label Hierachical Network for Compound Emotion Recognition	Jul 17, 2024	Emotion Recognition	—Unverified
Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition	Nov 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models	Dec 11, 2023	Emotion RecognitionFairness	—Unverified
Text-based Sentiment Analysis and Music Emotion Recognition	Oct 6, 2018	Emotion RecognitionMusic Emotion Recognition	—Unverified
The AffectToolbox: Affect Analysis for Everyone	Feb 23, 2024	Emotion Recognition	—Unverified
The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems	Jul 29, 2020	Emotion RecognitionFacial Emotion Recognition	—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