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 1051–1060 of 2041 papers

Title	Date	Tasks	Status
Describing emotions with acoustic property prompts for speech emotion recognition	Nov 14, 2022	Emotion RecognitionRetrieval	—Unverified
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement	Nov 12, 2022	Data AugmentationEmotion Recognition	—Unverified
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations	Nov 11, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition	Nov 9, 2022	Data AugmentationEmotion Recognition	—Unverified
Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection	Nov 9, 2022	Emotion RecognitionFacial Emotion Recognition	—Unverified
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features	Nov 8, 2022	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Speech-based emotion recognition with self-supervised models using attentive channel-wise correlations and label smoothing	Nov 3, 2022	Emotion Recognition	CodeCode Available
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search	Oct 31, 2022	Emotion RecognitionNeural Architecture Search	—Unverified
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition	Oct 29, 2022	Emotion RecognitionMulti-Task Learning	—Unverified
Effect of different splitting criteria on the performance of speech emotion recognition	Oct 26, 2022	Emotion RecognitionSentence	—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