Classification of pathological voice including severely noisy cases

In this paper we addressed the issue of classifying pathological voices from normal ones based on two different parameters, spectral slope and ratio of energies in harmonic and noise components (HNR), and artificial neural network (ANN). Voice data from normal peoples and patients were collected, then diagnosed and classified into three different categories (normal, relatively less noisy and severely noisy pathological data). The spectral slope and the HNR were computed and used to classify the severely noisy pathological voice from others first because of its much noise. Then artificial neural network was used as a classifier to discriminate the rest of data into normal and relatively less noisy pathological categories when common numerical parameters were used as inputs. And the classification results were evaluated by comparing the distribution characteristics of the spectral slope and HNR for all of data and analyzing the classification rates for the normal and relative less noisy pathological voices.