Automated speech analysis in depressive disorder: enhancing diagnosis and monitoringNeuberg, Aljaž (Avtor) Karakatič, Sašo (Mentor) DepressionClassificationMachine LearningData BalancingFeature SelectionThis paper investigates the automatic recognition of depression by integrating acoustic, linguistic and emotional features extracted from clinical interviews in the DAIC-WOZ dataset. A total of six classical machine learning classifiers such as Decision Tree, Random Forest, SVM, Gradient Boosting, AdaBoost and XGBoost were systematically evaluated under different class balancing methods (such as SMOTE, SMOTETomek and Random Undersampling) and feature selection strategies. The best model, a decision tree classifier with SMOTE-based balancing and a feature selection technique, achieved a weighted F1 score and accuracy of 0.78 with only eight selected features. These features included all three modalities, demonstrating the added benefit of a multimodal approach. The results suggest that even relatively simple models, when supported by careful preprocessing and dimensionality reduction, can provide accurate and interpretable predictions. This work emphasizes the importance of feature engineering and balancing techniques in clinical machine learning tasks and lays the foundation for future research on scalable and explainable depression detection systems.[A. Neuberg]20252025-09-26 14:44:10Magistrsko delo/naloga95573UDK: 621.38(043.2)COBISS_ID: 262077187sl