Investigation of the Effects of Speech Signal Length on Vocal Disorder Sorting Done Via Dynamic Pattern Modeling.

Objectives: Development of a noninvasive method for separating different vocal fold diseases is an important issue concerning vocal analysis. Due to the time variations along a pathologic vocal signal, application of dynamic pattern modeling tools is expected to help in the detection of defects that occur in the speech production mechanism.
Materials and Methods: In the present study, the hidden Markov model, which is a state space model, is employed to sort some of the vocal diseases. Moreover, this research mainly investigates the effects of the processed vocal signal lengths on the mentioned sorting task. To this end, the signal lengths of 1, 3, and 5 seconds of different disorders are used.
Results: The experimental results show that some pathologic conditions in vocal folds such as cyst, false vocal cord, and mass are more evident in continued voice production, and the recognition accuracies gained via dynamic modeling of pathologic voice signals with more lengths are considerably improved.
(Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)