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 101–110 of 2041 papers

Title	Date	Tasks	Status	Hype
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks	Jul 18, 2019	EEGEEG Emotion Recognition	CodeCode Available	1
EEG-Based Emotion Recognition Using Genetic Algorithm Optimized Multi-Layer Perceptron	Nov 4, 2021	EEGEEG Emotion Recognition	CodeCode Available	1
Cooperative Sentiment Agents for Multimodal Sentiment Analysis	Apr 19, 2024	DisentanglementEmotion Recognition	CodeCode Available	1
DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations	Oct 17, 2023	BenchmarkingEmotion Recognition	CodeCode Available	1
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain	Jun 1, 2022	BenchmarkingEmotion Recognition	CodeCode Available	1
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition	Mar 28, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
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
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition	Mar 21, 2024	Emotion RecognitionNeural Architecture Search	CodeCode Available	1
EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics	Aug 16, 2024	Decision MakingEmotion Recognition	CodeCode Available	1
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition	Jul 28, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1

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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