  Changes in speech and language are among the first signs of Parkinson's
disease (PD). Thus, clinicians have tried to identify individuals with PD from
their voices for years. Doctors can leverage AI-based speech assessments to
spot PD thanks to advancements in artificial intelligence (AI). Such AI systems
can be developed using machine learning classifiers that have been trained
using individuals' voices. Although several studies have shown reasonable
results in developing such AI systems, these systems would need more data
samples to achieve promising performance. This paper explores using deep
learning-based data generation techniques on the accuracy of machine learning
classifiers that are the core of such systems.
