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 331–340 of 2041 papers

Title	Date	Tasks	Status	Score
Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model	May 26, 2025	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition	Oct 7, 2014	Emotion RecognitionGeneral Classification	CodeCode Available	5
A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models	Apr 29, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
A Dataset for Speech Emotion Recognition in Greek Theatrical Plays	Mar 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	5
HoloFace: Augmenting Human-to-Human Interactions on HoloLens	Feb 1, 2018	AttributeEmotion Recognition	CodeCode Available	5
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography	Sep 11, 2019	EEGElectroencephalogram (EEG)	CodeCode Available	5
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond	Nov 6, 2018	Action LocalizationData Augmentation	CodeCode Available	5
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake	Mar 31, 2024	Audio Deepfake DetectionDeepFake Detection	CodeCode Available	5
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases	Sep 19, 2019	Emotion RecognitionGraph Neural Network	CodeCode Available	5
Group Affect Prediction Using Multimodal Distributions	Sep 17, 2017	Emotion RecognitionPrediction	CodeCode Available	5

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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