Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature

This study introduces a novel methodology for voice pathology detection using the publicly available Saarbr\"ucken Voice Database (SVD) database and a robust feature set combining commonly used acoustic handcrafted features with two novel ones: pitch difference (relative variation in fundamental frequency) and a NaN feature (failed fundamental frequency estimation). We evaluate six machine learning (ML) classifiers - support vector machine, k-nearest neighbors, naive Bayes, decision tree, random forest, and AdaBoost - using grid search for feasible hyperparameters of selected classifiers and 20480 different feature subsets. Top 1000 classifier-feature subset combinations for each classifier type are validated with repeated stratified cross-validation. To address class imbalance, we apply K-Means SMOTE to augment the training data. Our approach achieves outstanding performance, reaching 85.61%, 84.69% and 85.22% unweighted average recall (UAR) for females, males and combined results respectivelly. We intentionally omit accuracy as it is a highly biased metric for imbalanced data. This advancement demonstrates significant potential for clinical deployment of ML methods, offering a valuable supportive tool for an objective examination of voice pathologies. To enable an easier use of our methodology and to support our claims, we provide a publicly available GitHub repository with DOI 10.5281/zenodo.13771573. Finally, we provide a REFORMS checklist to enhance readability, reproducibility and justification of our approach.

@article{vrba2025_2410.10537,
  title={ Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature },
  author={ Jan Vrba and Jakub Steinbach and Tomáš Jirsa and Laura Verde and Roberta De Fazio and Yuwen Zeng and Kei Ichiji and Lukáš Hájek and Zuzana Sedláková and Zuzana Urbániová and Martin Chovanec and Jan Mareš and Noriyasu Homma },
  journal={arXiv preprint arXiv:2410.10537},
  year={ 2025 }
}