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.

许多研究提出了通过语音分析检测帕金森病 （PD） 的方法。然而，现有的语料库通常缺乏前驱记录，样本量小，并且缺乏纵向数据。公开披露其 PD 诊断结果的名人的语音样本提供了纵向数据，从而允许创建新的语料库 ParkCeleb。我们收集了 40 名 PD 受试者和 40 名对照受试者的视频，并分析了从诊断前 10 年到诊断后 20 年不断变化的言语特征。我们的纵向分析侧重于 15 名 PD 受试者和 15 名对照受试者，揭示了音高变化、停顿持续时间、语速和音节持续时间等特征，表明 PD 进展。在前驱期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 AUC 分别为 0.72 和 0.75，诊断后 0.93。本研究强调了早期检测方法的潜力，有助于临床试验中的治疗反应识别和筛选。