{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/reproducible-machine-learning-based-voice","title":"Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature","arxiv_id":"2410.10537","date":"2024-10-14","proceeding":null,"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":"Purpose: We introduce a novel methodology for voice pathology detection using the publicly available Saarbr\\\"ucken Voice Database (SVD) 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 20480 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 SMOTE to augment the training data. Results: Our approach achieves 85.61%, 84.69% and 85.22% unweighted average recall (UAR) 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","url_abs":"https://arxiv.org/abs/2410.10537v3","url_pdf":"https://arxiv.org/pdf/2410.10537v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"reproducible-machine-learning-based-voice","repo_url":"https://github.com/aailab-uct/automated-robust-and-reproducible-voice-pathology-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"voice-pathology-detection","task_name":"Voice pathology detection"}],"methods":[{"method_slug":"smote","method_name":"SMOTE"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/voice-pathology-detection-on-saarbruecken-2","task":"Voice pathology detection","dataset":"Saarbruecken Voice Database (females)","model":"SVM","rank_in_archive_order":1,"of":1,"metrics":{"UAR":"85.44%"},"uses_additional_data":false},{"leaderboard":"/sota/voice-pathology-detection-on-saarbruecken-1","task":"Voice pathology detection","dataset":"Saarbruecken Voice Database (males)","model":"SVM","rank_in_archive_order":1,"of":1,"metrics":{"UAR":"84.10%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}