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 861–870 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild	Aug 16, 2018	Emotion RecognitionFacial Emotion Recognition	—Unverified
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data	May 25, 2023	Emotion RecognitionSentiment Analysis	—Unverified
Emotion Recognition In Persian Speech Using Deep Neural Networks	Apr 28, 2022	BIG-bench Machine LearningEmotion Recognition	—Unverified
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition	Apr 26, 2022	DiversityEmotion Recognition	—Unverified
Evaluation of Speaker Anonymization on Emotional Speech	Apr 15, 2023	Automatic Speech RecognitionEmotion Recognition	—Unverified
Event Based Emotion Classification for News Articles	Oct 1, 2016	ArticlesClassification	—Unverified
EventFormer: AU Event Transformer for Facial Action Unit Event Detection	Mar 12, 2022	Emotion RecognitionEvent Detection	—Unverified
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition	Sep 24, 2024	Domain AdaptationEEG	—Unverified
EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation	Jan 13, 2024	Emotion RecognitionKnowledge Distillation	—Unverified
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods	Aug 28, 2019	Emotion Recognitionfeature selection	—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