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 1321–1330 of 2041 papers

Title	Date	Tasks	Status
Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter	Jun 1, 2021	ClassificationEmotion Recognition	—Unverified
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning	Aug 13, 2017	Cross-corpusEmotion Recognition	—Unverified
Towards Subject Agnostic Affective Emotion Recognition	Oct 20, 2023	Domain AdaptationEEG	—Unverified
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations	Mar 28, 2022	ClassificationDenoising	—Unverified
Toward the application of XAI methods in EEG-based systems	Oct 12, 2022	Brain Computer InterfaceClassification	—Unverified
Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices	Aug 22, 2021	DiagnosticEmotion Recognition	—Unverified
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study	Dec 16, 2020	Emotion ClassificationEmotion Recognition	—Unverified
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm	Sep 12, 2018	Emotion Recognition	—Unverified
Training speech emotion classifier without categorical annotations	Oct 14, 2022	ClassificationEmotion Recognition	—Unverified
Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation	Oct 30, 2018	Domain AdaptationEmotion 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