Improving the exactness of the classification of healthy and pathological continuous speech

A number of experiments were made in the field of speech diagnostic analysis in which researchers wanted to examine whether it was the acoustic characteristics of sustained voice or continuous speech that were more appropriate for distinguishing healthy from pathological voice. While in phoniatric practice, doctors mainly use continuous speech, we also wanted to concentrate on the examination of continuous speech. In this paper we present a series of classification experiments showing how it is possible to separate healthy from pathological speech automatically, on the basis of continuous speech. It is demonstrated that the results of the automatic classification of healthy vs. pathological voice improved to a large extent by a multi-step processing methodology, in which most examples in which uncertainties occurred in the measurement of the acoustic parameters can be accounted for separately. That multistep processing could be especially useful when pathological data is not enough for statistical sense.