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 111–120 of 2041 papers

Title	Date	Tasks	Status	Hype
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition	Nov 18, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction	Nov 8, 2023	Emotion RecognitionGraph Neural Network	CodeCode Available	1
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations	Oct 31, 2023	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1
SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations	Oct 25, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition	Oct 18, 2023	Audio ClassificationContrastive Learning	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
Crowdsourced and Automatic Speech Prominence Estimation	Oct 12, 2023	Emotion Recognitiontext-to-speech	CodeCode Available	1
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals	Oct 11, 2023	EEGElectroencephalogram (EEG)	CodeCode Available	1
In the Blink of an Eye: Event-based Emotion Recognition	Oct 6, 2023	Emotion Recognition	CodeCode Available	1
InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models	Sep 21, 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