  This paper deals with a complex acoustic analysis of phonation in patients
with Parkinson's disease (PD) with a special focus on estimation of disease
progress that is described by 7 different clinical scales ,e. g. Unified
Parkinson's disease rating scale or Beck depression inventory. The analysis is
based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using
classification and regression trees we estimated all clinical scores with
maximal error lower or equal to 13 %. Best estimation was observed in the case
of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %. Finally,
we proposed a binary classification based on random forests that is able to
identify Parkinson's disease with sensitivity SEN = 92.86 % (SPE = 85.71 %).
The parametrization process was based on extraction of 107 speech features
quantifying different clinical signs of hypokinetic dysarthria present in PD.
