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 831–840 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition under Consideration of the Emotion Component Process Model	Jul 27, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Emotion recognition techniques with rule based and machine learning approaches	Feb 28, 2021	BIG-bench Machine LearningCultural Vocal Bursts Intensity Prediction	—Unverified
Enhancing Multimodal Affective Analysis with Learned Live Comment Features	Oct 21, 2024	Contrastive LearningEmotion Recognition	—Unverified
Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment	Dec 30, 2024	cross-modal alignmentEmotion Recognition	—Unverified
Comparison of Gender- and Speaker-adaptive Emotion Recognition	May 1, 2014	AttributeEmotion Classification	—Unverified
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features	Oct 16, 2024	Emotion RecognitionSelf-Supervised Learning	—Unverified
A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let's Go Bus Information System	May 1, 2012	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Enhancing Student Engagement in Online Learning through Facial Expression Analysis and Complex Emotion Recognition using Deep Learning	Nov 17, 2023	Emotion Recognition	—Unverified
Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks	Feb 29, 2020	Emotion Recognition	—Unverified
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition	Apr 13, 2021	Emotion RecognitionFeature Engineering	—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