Diagnosis of voices disorders using MEL scaled WPT and functional link neural network

Abstract

Nowadays voice disorders are increasing dramatically due to the modern way of life. Most of the voice disorders cause changes in the voice signal. Acoustic analysis on the speech signal could be a useful tool for diagnosing voice disorders. This paper applies Mel-scaled wavelet packet transform (Mel-scaled WPT) based features to perform accurate diagnosis of voice disorders. A Functional Link Neural Network (FLNN) is developed to test the usefulness of the suggested features. Two simple modifications are newly proposed in the FLNN architecture to improve the classification accuracy. In the first architecture, a hidden layer is newly introduced in a FLNN and trained by Back Propagation (BP) procedure. In the second architecture, the Integral and Derivative controller concepts are introduced to the neurons in the hidden layer and the network is trained by BP procedure. The performance is compared with conventional neural network model. The results prove that the proposed FLNN gives very promising classification accuracy and suggested features can be employed clinically to diagnose the voice disorders.