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 1241–1250 of 2041 papers

Title	Date	Tasks	Status	Hype
A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese	Aug 17, 2021	Emotion RecognitionLanguage Modeling	CodeCode Available	0
HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition	Aug 7, 2021	Emotion Recognition	CodeCode Available	1
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation	Aug 5, 2021	Emotion ClassificationEmotion Recognition	—Unverified	0
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition	Aug 4, 2021	ClassificationEmotion Classification	—Unverified	0
The Role of Phonetic Units in Speech Emotion Recognition	Aug 2, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified	0
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities	Aug 1, 2021	Emotion Recognition	CodeCode Available	1
Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition	Jul 31, 2021	Emotion Recognition	CodeCode Available	0
Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition	Jul 28, 2021	Deep AttentionEEG	—Unverified	0
Emotion Stimulus Detection in German News Headlines	Jul 27, 2021	Emotion RecognitionSentence	—Unverified	0
Emotion Recognition under Consideration of the Emotion Component Process Model	Jul 27, 2021	Emotion ClassificationEmotion Recognition	—Unverified	0

Show:10 25 50

← PrevPage 125 of 205Next →

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