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 761–770 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition in Conversation using Probabilistic Soft Logic	Jul 14, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces	May 25, 2024	Emotion RecognitionRepresentation Learning	—Unverified
EEG based Emotion Recognition of Image Stimuli	Sep 11, 2019	EEGElectroencephalogram (EEG)	—Unverified
Emotion Recognition In Persian Speech Using Deep Neural Networks	Apr 28, 2022	BIG-bench Machine LearningEmotion Recognition	—Unverified
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
EEG based Emotion Recognition: A Tutorial and Review	Mar 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
Bias and Fairness on Multimodal Emotion Detection Algorithms	May 11, 2022	Emotion RecognitionFairness	—Unverified
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis	Jan 16, 2024	DenoisingEmotional Speech Synthesis	—Unverified
Edge Based Grid Super-Imposition for Crowd Emotion Recognition	Aug 7, 2016	Edge DetectionEmotion 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