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 1581–1590 of 2041 papers

Title	Date	Tasks	Status
Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training	Nov 11, 2021	Emotion RecognitionMultilabel Text Classification	—Unverified
Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns	Aug 1, 2014	Emotion RecognitionInformation Retrieval	—Unverified
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search	Oct 31, 2022	Emotion RecognitionNeural Architecture Search	—Unverified
Multi-Microphone and Multi-Modal Emotion Recognition in Reverberant Environment	Sep 14, 2024	Emotion Recognition	—Unverified
Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture	Jun 5, 2024	Emotion ClassificationEmotion Recognition	—Unverified
Multimodal Affect Recognition using Kinect	Jul 9, 2016	Emotion Recognition	—Unverified
Multimodal Alignment and Fusion: A Survey	Nov 26, 2024	Data IntegrationDiversity	—Unverified
Multimodal and Multi-view Models for Emotion Recognition	Jun 24, 2019	Emotion RecognitionMULTI-VIEW LEARNING	—Unverified
Multi-modal embeddings using multi-task learning for emotion recognition	Sep 10, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Multimodal Emotion-Cause Pair Extraction in Conversations	Oct 15, 2021	ArticlesEmotion Cause Extraction	—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