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 1551–1560 of 2041 papers

Title	Date	Tasks	Status
An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions	Mar 5, 2021	Emotion Recognition	—Unverified
An Event-comment Social Media Corpus for Implicit Emotion Analysis	May 1, 2020	ClassificationEmotion Classification	—Unverified
A New Amharic Speech Emotion Dataset and Classification Benchmark	Jan 7, 2022	ClassificationEmotion Recognition	—Unverified
A new model for the implementation of positive and negative emotion recognition	May 1, 2019	EEGElectroencephalogram (EEG)	—Unverified
A New View of Multi-modal Language Analysis: Audio and Video Features as Text ``Styles''	Apr 1, 2021	Emotion RecognitionSentiment Analysis	—Unverified
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset	Apr 2, 2023	Emotion RecognitionFacial Emotion Recognition	—Unverified
An Explainable Fast Deep Neural Network for Emotion Recognition	Jul 20, 2024	AttributeEmotion Classification	—Unverified
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications	May 9, 2023	Emotion Recognitionintent-classification	—Unverified
Angry or Climbing Stairs? Towards Physiological Emotion Recognition in the Wild	Nov 12, 2018	Emotion Recognition	—Unverified
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations	Dec 1, 2020	Emotion RecognitionEmotion Recognition in Conversation	—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