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 551–560 of 2041 papers

Title	Date	Tasks	Status
A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition	Jan 5, 2023	Emotion Recognition	—Unverified
Climate and Weather: Inspecting Depression Detection via Emotion Recognition	Apr 29, 2022	Depression DetectionEmotion Recognition	—Unverified
A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices	Aug 5, 2022	Emotion RecognitionFacial Emotion Recognition	—Unverified
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding	Nov 4, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics	Apr 27, 2025	ArticlesEmotion Recognition	—Unverified
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition	Jun 12, 2024	Contrastive LearningEEG	—Unverified
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network	Jul 4, 2022	Efficient Neural NetworkEmotion Recognition	—Unverified
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition	Dec 1, 2021	Emotion RecognitionGender Classification	—Unverified
A Novel Dialect-Aware Framework for the Classification of Arabic Dialects and Emotions	Feb 13, 2025	Emotion ClassificationEmotion Recognition	—Unverified
A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition	Mar 19, 2017	Emotion Recognition	—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