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 121–130 of 2041 papers

Title	Date	Tasks	Status	Hype	Score
Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition	Nov 9, 2021	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1	5
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction	Nov 8, 2023	Emotion RecognitionGraph Neural Network	CodeCode Available	1	5
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition	Aug 4, 2023	Cross-corpusDomain Adaptation	CodeCode Available	1	5
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents	May 4, 2020	BIG-bench Machine LearningEmotion Recognition	CodeCode Available	1	5
Facial Affective Behavior Analysis with Instruction Tuning	Apr 7, 2024	Emotion RecognitionInstruction Following	CodeCode Available	1	5
A Supervised Information Enhanced Multi-Granularity Contrastive Learning Framework for EEG Based Emotion Recognition	May 12, 2024	Contrastive LearningEEG	CodeCode Available	1	5
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction	Jul 1, 2020	Emotion-Cause Pair ExtractionEmotion Recognition	CodeCode Available	1	5
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks	Jul 18, 2019	EEGEEG Emotion Recognition	CodeCode Available	1	5
A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora	Nov 18, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset	Dec 30, 2021	Autonomous DrivingEmotion Recognition	CodeCode Available	1	5

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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