Feature analysis of dysphonia speech for monitoring Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder that has no known cure and no known prevention. Early detection is crucial in order to slow down the progress. In the past 10 years, interest in PD analysis has visibly increased. Speech impairment affects the majority of people with Parkinson's (PWP). New features and machine learning algorithms were proposed to help diagnose PD and to measure a patient's progress. Using sustained vowel /a/ recordings, we identified a more prominent set of Mel-Frequency Cepstral Coefficient (MFCC) and Intrinsic Mode Functions (IMF), and other parameters that can best represent the characteristics of Parkinson's dysphonia to assist with the diagnosis process. For higher quality audio signals, there is a visible difference in the higher MFCC coefficients, the wider spectrum bandwidth in the first four IMFs of PWP, and higher power intensity in the healthy subjects. We also found that even when the signals are downsampled into toll-quality, the distinguishable MFCC and IMF features were largely maintained. This enabled a whole possibility of providing telemedicine for PWP.