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 531–540 of 2041 papers

Title	Date	Tasks	Status
A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition	Dec 1, 2017	Emotion RecognitionSpeech Emotion Recognition	—Unverified
AHD ConvNet for Speech Emotion Classification	Jun 10, 2022	ClassificationEmotion Classification	—Unverified
Combining Heterogeneous User Generated Data to Sense Well-being	Dec 1, 2016	Emotion Recognition	—Unverified
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface	Sep 7, 2022	Brain Computer InterfaceEmotion Recognition	—Unverified
Acoustic-to-articulatory Speech Inversion with Multi-task Learning	May 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition	Jun 12, 2022	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation	Jun 1, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
A Graph Isomorphism Network with Weighted Multiple Aggregators for Speech Emotion Recognition	Jul 3, 2022	Emotion RecognitionGraph Neural Network	—Unverified
A Bilingual Attention Network for Code-switched Emotion Prediction	Dec 1, 2016	Emotion Recognition	—Unverified
Domain Adversarial for Acoustic Emotion Recognition	Apr 20, 2018	AttributeEmotion 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