Romana Amrit, Bandon John, Perez Matthew, Gutierrez Stephanie, Richter Richard, Roberts Angela, Provost Emily Mower
Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA.
Interspeech. 2021 Aug-Sep;2021:1907-1911. doi: 10.21437/interspeech.2021-1694.
Parkinson's disease (PD) is a central nervous system disorder that causes motor impairment. Recent studies have found that people with PD also often suffer from cognitive impairment (CI). While a large body of work has shown that speech can be used to predict motor symptom severity in people with PD, much less has focused on cognitive symptom severity. Existing work has investigated if acoustic features, derived from speech, can be used to detect CI in people with PD. However, these acoustic features are general and are not targeted toward capturing CI. Speech errors and disfluencies provide additional insight into CI. In this study, we focus on read speech, which offers a controlled template from which we can detect errors and disfluencies, and we analyze how errors and disfluencies vary with CI. The novelty of this work is an automated pipeline, including transcription and error and disfluency detection, capable of predicting CI in people with PD. This will enable efficient analyses of how cognition modulates speech for people with PD, leading to scalable speech assessments of CI.
帕金森病（PD）是一种导致运动功能障碍的中枢神经系统疾病。最近的研究发现，帕金森病患者也经常遭受认知障碍（CI）。虽然大量研究表明，语音可用于预测帕金森病患者的运动症状严重程度，但针对认知症状严重程度的研究却少得多。现有研究探讨了从语音中提取的声学特征是否可用于检测帕金森病患者的认知障碍。然而，这些声学特征较为通用，并非专门用于捕捉认知障碍。言语错误和不流畅性为认知障碍提供了更多线索。在本研究中，我们聚焦于朗读语音，它提供了一个可控的模板，我们可以从中检测错误和不流畅性，并分析错误和不流畅性如何随认知障碍而变化。这项工作的新颖之处在于一个自动化流程，包括转录以及错误和不流畅性检测，能够预测帕金森病患者的认知障碍。这将有助于高效分析认知如何调节帕金森病患者的言语，从而实现对认知障碍的可扩展言语评估。
