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

Purpose: We introduce a novel methodology for voice pathology detection using the publicly available Saarbrücken Voice Database and a robust feature set combining commonly used acoustic handcrafted features with two novel ones: pitch difference (relative variation in fundamental frequency) and NaN feature (failed fundamental frequency estimation). Methods: We evaluate six machine learning (ML) algorithms—support vector machine, k-nearest neighbors, naive Bayes, decision tree, random forest, and AdaBoost—using grid search for feasible hyperparameters and 20 480 different feature subsets. Top 1000 classification models—feature subset combinations for each ML algorithm are validated with repeated stratified cross-validation. To address class imbalance, we apply k-means synthetic minority oversampling technique to augment the training data. Results: Our approach achieves 85.61%, 84.69%, and 85.22% unweighted average recall for females, males, and combined results, respectively. We intentionally omit accuracy as it is a highly biased metric for imbalanced data. Conclusion: Our study demonstrates that by following the proposed methodology and feature engineering, there is a potential in detection of various voice pathologies using ML models applied to the simplest vocal task, a sustained utterance of the vowel /a:/. To enable 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.