Application of autoregressive decomposition and pole tracking to pathological voice signals

In this paper, it is presented an insight of the effects of the application of autoregressive (AR) decomposition and pole tracking to voice signals. The AR model is used to decompose the signals in a set of poles which has a correspondence to the peaks of the signals power spectral density function (PSD). The aim of this work is to show the differences in the behavior of these poles for voice signals collected from two groups of people, one with healthy glottal tract and another with nodule pathology in vocal folds.