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 1271–1280 of 2041 papers

Title	Date	Tasks	Status
Pretext Tasks selection for multitask self-supervised speech representation learning	Jul 1, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Affective Image Content Analysis: Two Decades Review and New Perspectives	Jun 30, 2021	Emotional IntelligenceEmotion Recognition	—Unverified
Use of Variational Inference in Music Emotion Recognition	Jun 27, 2021	Emotion RecognitionMusic Emotion Recognition	—Unverified
A Circular-Structured Representation for Visual Emotion Distribution Learning	Jun 23, 2021	Emotion Recognition	—Unverified
Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences	Jun 19, 2021	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement	Jun 19, 2021	DisentanglementEmotion Recognition	—Unverified
Learning a Facial Expression Embedding Disentangled From Identity	Jun 19, 2021	Data AugmentationEmotion Recognition	—Unverified
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction	Jun 17, 2021	Common Sense ReasoningEmotion Cause Extraction	—Unverified
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems	Jun 16, 2021	Data AugmentationEmotion Recognition	—Unverified
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model	Jun 16, 2021	Emotion RecognitionLanguage Modeling	—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