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

• URL: http://arxiv.org/abs/2410.10537v2
• Date: Mon, 03 Feb 2025 12:57:09 GMT
• Title: Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature
• Authors: Jan Vrba, Jakub Steinbach, Tomáš Jirsa, Laura Verde, Roberta De Fazio, Yuwen Zeng, Kei Ichiji, Lukáš Hájek, Zuzana Sedláková, Zuzana Urbániová, Martin Chovanec, Jan Mareš, Noriyasu Homma,
• Abstract summary: This study introduces a novel methodology for voice pathology detection using the publicly available Saarbr"ucken Voice Database (SVD) database. We evaluate six machine learning (ML) classifiers and apply K-Means SMOTE to address class imbalance. Our approach outstanding performance, reaching 85.61%, 84.69% and 85.22% unweighted average recall (UAR) for females, males and combined results respectivelly.
• Score: 1.7779568951268254
• License:
• Abstract: 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.

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