Attention-based acoustic feature fusion network for depression detection
Xu X, Wang Y, Wei X, Wang F, Zhang X. Attention-based acoustic feature fusion network for depression detection. Neurocomputing 2024, 601: 128209. DOI: 10.1016/j.neucom.2024.128209.Peer-Reviewed Original ResearchFeature fusion networkFusion networkDepression detectionAdvanced machine learning paradigmsDeep neural networksMachine learning paradigmLSTM-attention mechanismSpeech databaseFeature modelSpeech featuresNeural networkAbundance of informationBoost performanceLearning paradigmImproved detection methodAuditory dataAcoustic featuresDetection methodFeature processingAdjustment moduleNetworkLSTM-AttentionResearch directionsEffective detectionFeaturesEnhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
Liang L, Wang Y, Ma H, Zhang R, Liu R, Zhu R, Zheng Z, Zhang X, Wang F. Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning. Frontiers In Psychiatry 2024, 15: 1422020. PMID: 39355380, PMCID: PMC11442283, DOI: 10.3389/fpsyt.2024.1422020.Peer-Reviewed Original ResearchVocal acoustic featuresHealthy control groupSeverity of depressive symptomsTotal depression scoreAcoustic featuresClassification accuracyMDD groupDepressive disorderAnxiety comorbiditiesDepression prediction modelDeep learning methodsDepressive symptomsDepression scoresHC groupSpeech characteristicsMean Absolute Error(MAEDepressionNeural networkEnhanced classificationControl groupLearning methodsMachine learningClassification modelOpen-source algorithmAbsolute error(MAE