  In this paper, we envision a web-based framework that can help anyone,
anywhere around the world record a short speech task, and analyze the recorded
data to screen for Parkinson's disease (PD). We collected data from 726 unique
participants (262 PD, 38% female; 464 non-PD, 65% female; average age: 61) --
from all over the US and beyond. A small portion of the data was collected in a
lab setting to compare quality. The participants were instructed to utter a
popular pangram containing all the letters in the English alphabet "the quick
brown fox jumps over the lazy dog..". We extracted both standard acoustic
features (Mel Frequency Cepstral Coefficients (MFCC), jitter and shimmer
variants) and deep learning based features from the speech data. Using these
features, we trained several machine learning algorithms. We achieved 0.75 AUC
(Area Under The Curve) performance on determining presence of self-reported
Parkinson's disease by modeling the standard acoustic features through the
XGBoost -- a gradient-boosted decision tree model. Further analysis reveal that
the widely used MFCC features and a subset of previously validated dysphonia
features designed for detecting Parkinson's from verbal phonation task
(pronouncing 'ahh') contains the most distinct information. Our model performed
equally well on data collected in controlled lab environment as well as 'in the
wild' across different gender and age groups. Using this tool, we can collect
data from almost anyone anywhere with a video/audio enabled device,
contributing to equity and access in neurological care.
