Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation.

中文翻译：

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基于声门流声学分析的帕金森病和老化语音鉴别方法

言语由轴向神经运动系统控制，因此，它对帕金森病 (PD) 等神经退行性疾病的影响高度敏感。患有 PD 的患者在言语方面表现出重要的变化，表现为发声、发音、韵律和流利度。这些变化可以使用统计方法对从语音的声门、频谱、倒谱或分形描述中获得的特征进行评估。这项工作引入了一种基于信息论 (IT) 的评估范式，以区分 PD 和老化对声门振幅分布的影响。该研究在一个数据库上进行，该数据库包括 48 名 PD 患者（24 名男性，24 名女性）、48 名年龄匹配的健康对照（HC，24 名男性，24 名女性）和 48 名中年正常受试者（NS，24 名男性，24 名女性） ）。从研究中可以得出结论，分层聚类 (HiCl) 方法在 PD 患者和 NS 受试者的发声之间产生了明显的分离（男性和女性子集的准确度为 89.6%），但 PD 患者和 HC 受试者之间的分离是效率较低（男性子集的准确率为 75.0%，女性子集的准确率为 70.8%）。相反，使用特征选择和支持向量机 (SVM) 分类，PD 和 HC 之间的差异显着提高（男性子集的准确率为 94.8%，女性子集的准确率为 92.8%）。这种改进主要是通过特征选择来推动的，但代价是信息和泛化损失。结果表明，随着年龄的增长，语音恶化可能会影响 HC 发声，从而减少其与 PD 发声的差异。