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 1801–1810 of 2041 papers

Title	Date	Tasks	Status
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period	Nov 22, 2020	Brain Computer InterfaceDeep Learning	—Unverified
deep learning of segment-level feature representation for speech emotion recognition in conversations	Feb 5, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Deep Metric Structured Learning For Facial Expression Recognition	Jan 18, 2020	Emotion RecognitionFacial Expression Recognition	—Unverified
Deep Multimodal Learning for Emotion Recognition in Spoken Language	Feb 22, 2018	Emotion RecognitionSentence	—Unverified
Deep Net Features for Complex Emotion Recognition	Oct 31, 2018	automatic-speech-translationEmotion Recognition	—Unverified
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data	Mar 9, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition	Jul 28, 2021	Deep AttentionEEG	—Unverified
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends	Jan 2, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Deep Residual Local Feature Learning for Speech Emotion Recognition	Nov 19, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Deep scattering network for speech emotion recognition	May 11, 2021	Emotion RecognitionSpeech 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