Favaro Anna, Butala Ankur, Thebaud Thomas, Villalba Jesús, Dehak Najim, Moro-Velázquez Laureano
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA.
Center for Language and Speech Processing, The Johns Hopkins University, Baltimore, MD, USA.
NPJ Parkinsons Dis. 2024 Oct 27;10(1):207. doi: 10.1038/s41531-024-00817-9.
Numerous studies proposed methods to detect Parkinson's disease (PD) via speech analysis. However, existing corpora often lack prodromal recordings, have small sample sizes, and lack longitudinal data. Speech samples from celebrities who publicly disclosed their PD diagnosis provide longitudinal data, allowing the creation of a new corpus, ParkCeleb. We collected videos from 40 subjects with PD and 40 controls and analyzed evolving speech features from 10 years before to 20 years after diagnosis. Our longitudinal analysis, focused on 15 subjects with PD and 15 controls, revealed features like pitch variability, pause duration, speech rate, and syllable duration, indicating PD progression. Early dysarthria patterns were detectable in the prodromal phase, with the best classifiers achieving AUCs of 0.72 and 0.75 for data collected ten and five years before diagnosis, respectively, and 0.93 post-diagnosis. This study highlights the potential for early detection methods, aiding treatment response identification and screening in clinical trials.
众多研究提出了通过语音分析检测帕金森病（PD）的方法。然而，现有的语料库往往缺乏前驱期记录，样本量小，且缺乏纵向数据。公开披露其PD诊断的名人的语音样本提供了纵向数据，从而得以创建一个新的语料库ParkCeleb。我们收集了40名PD患者和40名对照者的视频，并分析了从诊断前10年到诊断后20年语音特征的变化。我们针对15名PD患者和15名对照者进行的纵向分析揭示了诸如音高变异性、停顿持续时间、语速和音节持续时间等特征，表明了PD的进展情况。在前驱期即可检测到早期构音障碍模式，对于诊断前10年和5年收集的数据，最佳分类器的曲线下面积（AUC）分别达到0.72和0.75，诊断后为0.93。本研究突出了早期检测方法的潜力，有助于在临床试验中识别治疗反应和进行筛查。
