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 1061–1070 of 2041 papers

Title	Date	Tasks	Status
Pretrained audio neural networks for Speech emotion recognition in Portuguese	Oct 26, 2022	Data AugmentationEmotion Recognition	CodeCode Available
Multilevel Transformer For Multimodal Emotion Recognition	Oct 26, 2022	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Knowledge Transfer For On-Device Speech Emotion Recognition with Neural Structured Learning	Oct 26, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Towards emotion recognition for virtual environments: an evaluation of EEG features on benchmark dataset	Oct 25, 2022	EEGElectroencephalogram (EEG)	—Unverified
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context	Oct 25, 2022	EEGElectroencephalogram (EEG)	—Unverified
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network	Oct 22, 2022	Emotion RecognitionSpeech Emotion Recognition	—Unverified
PERI: Part Aware Emotion Recognition In The Wild	Oct 18, 2022	Emotion Recognition	—Unverified
Modelling Emotion Dynamics in Song Lyrics with State Space Models	Oct 17, 2022	Emotion RecognitionMusic Emotion Recognition	—Unverified
Training speech emotion classifier without categorical annotations	Oct 14, 2022	ClassificationEmotion Recognition	—Unverified
Toward the application of XAI methods in EEG-based systems	Oct 12, 2022	Brain Computer InterfaceClassification	—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