用新声学特征提升语音病理检测准确率，方法可复现。
Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature
- 结合传统与新型声学特征，创新提出音高差和无效估计特征。
- 在性别和总体数据上分别达到85.61%、84.69%、85.22%的平均召回率。
- 提供完整代码与数据，适合想复现或应用语音病理检测的研究者。
目的：本文引入一种基于公开数据集萨尔布吕肯语音数据库（SVD）的语音病理检测新方法，结合常用声学特征与两项新特征——音高差（基频相对变化）和NaN特征（基频估计失败标志）。方法：评估六种机器学习算法（支持向量机、k近邻、朴素贝叶斯、决策树、随机森林、AdaBoost），通过网格搜索确定超参数，并测试20480种特征子集组合；对每种算法选出前1000个最佳分类模型，采用重复分层交叉验证进行验证；为缓解类别不平衡问题，使用K-Means SMOTE增强训练数据。结果：女性、男性及合并数据的未加权平均召回率（UAR）分别为85.61%、84.69%和85.22%。因准确率在不平衡数据中具有高度偏差，故未报告。结论：本研究证明，遵循该方法与特征工程，仅使用最简单的持续发元音 /a:/ 任务，即可有效检测多种语音病理。为便于复现并支持结论，我们公开了带有DOI 10.5281/zenodo.13771573的GitHub仓库，并提供REFORMS清单以提升方法透明度与可复现性。
原文摘要 · Abstract (English)
Purpose: We introduce a novel methodology for voice pathology detection using the publicly available Saarbrücken 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
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