  Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic
dysarthria (HD). In this work, we analysed the power of conventional speech
features quantifying imprecise articulation, dysprosody, speech dysfluency and
speech quality deterioration extracted from a specialized poem recitation task
to discriminate dysarthric and healthy speech. For this purpose, 152 speakers
(53 healthy speakers, 99 PD patients) were examined. Only mildly strong
correlation between speech features and clinical status of the speakers was
observed. In the case of univariate classification analysis, sensitivity of
62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech
dysfluency) and 59.60% (speech quality deterioration) was achieved.
Multivariate classification analysis improved the classification performance.
Sensitivity of 83.42% using only two features describing imprecise articulation
and speech quality deterioration in HD was achieved. We showed the promising
potential of the selected speech features and especially the use of poem
recitation task to quantify and identify HD in PD.
