  Parkinson's disease (PD) is the most widespread movement condition and the
second most common neurodegenerative disorder, following Alzheimer's. Movement
symptoms and imaging techniques are the most popular ways to diagnose this
disease. However, they are not accurate and fast and may only be accessible to
a few people. This study provides an autonomous system, i.e., PD-ADSV, for
diagnosing PD based on voice signals, which uses four machine learning
classifiers and the hard voting ensemble method to achieve the highest
accuracy. PD-ADSV is developed using Python and the Gradio web framework.

Sound;Machine Learning;Audio and Speech Processing;Signal Processing