Using a podcast application to collect high-quality speech data online for acoustic analysis in people with Parkinson's disease

Parkinson's disease is a neurodegenerative syndrome that results in various movement abnormalities including a resting tremor, bradykinesia (slow or reduced range of movement), rigidity due to increased muscle tone, a delay in the initiation of movements, and disturbances of postural reflexes. This can lead to secondary conditions such as depression, dementia, swallowing difficulties, and a speech disorder called hypokinetic dysarthria. The aim of this doctoral project is to find robust and 'trackable' markers in hypokinetic dysarthria associated with Parkinson’s disease across two time points. These markers could indicate a change in Parkinson's disease motor symptoms. The original plan for this study was to take speech recordings of people with Parkinson's disease and age matched controls, face to face in a recording studio, for acoustic analysis. However, with the onset of the COVID-19 pandemic, a change of plan was required. The move to online data collection presented several new challenges, while also having a positive effect on participant numbers. This case study focuses on the data collection process used to collect speech data online from people with Parkinson's disease and a control group for the purpose of acoustic analysis. Issues encountered in collecting reliable speech data online are discussed, including the key factors of consideration such as audio quality, ease of the data collection process for participants, and the available methods to record speech data remotely. Finally, the application of this method of data collection in other linguistics studies and wider use in social sciences is outlined.