Applying Deep Learning for the Examination of Voice Disorders in the Identification of Parkinson's Disease

Parkinson's disease is a degenerative neurological condition marked by difficulties with coordination, tremors, and rigidity in the muscles. Positively, speech recordings show potential as a precocious biomarker for Parkinson's disease, able to identify minute alterations prior to the manifestation of other symptoms. The main objective of this research is to create a machine learning model that can analyse speech recordings and reliably determine whether Parkinson's disease is present. Key questions that the model aims to answer include how well it detects early signs of Parkinson's disease, how well it distinguishes between voice samples from healthy individuals and those from early stages of Parkinson's disease, and whether or not accuracy will vary depending on variables like the length or calibre of voice recordings. The primary goal is to use deep learning methods to analyse voice data to detect Parkinson's disease early on.