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 1781–1790 of 2041 papers

Title	Date	Tasks	Status
Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition	Jul 1, 2024	Emotion RecognitionPrediction	CodeCode Available
Progressive Neural Networks for Transfer Learning in Emotion Recognition	Jun 10, 2017	Emotion RecognitionTransfer Learning	CodeCode Available
Multimodal Embeddings from Language Models	Sep 10, 2019	Emotion RecognitionLanguage Modeling	CodeCode Available
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification	Oct 1, 2018	Emotion ClassificationEmotion Recognition	CodeCode Available
The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variability	Aug 21, 2024	Cross-corpusEmotion Recognition	CodeCode Available
Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model	Nov 16, 2024	Data AugmentationEmotion Recognition	CodeCode Available
A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition	Jan 4, 2021	Cross-corpusDeep Reinforcement Learning	CodeCode Available
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition	Jul 18, 2022	Cross-corpusEmotion Recognition	CodeCode Available
Crossmodal ASR Error Correction with Discrete Speech Units	May 26, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages	Dec 15, 2018	Cross-corpusEmotion Recognition	CodeCode Available

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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