TITLE:
Classification of Normal and Pathological Voice Using SVM and RBFNN
AUTHORS:
V. Sellam, J. Jagadeesan
KEYWORDS:
Terms—Pitch; Formants; Jitter; Shimmer; Reflection Coefficients; SVM; RBFNN
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.5 No.1,
February
10,
2014
ABSTRACT: The identification
and classification of pathological voice are still a challenging area of research in speech processing. Acoustic features
of speech are used mainly to discriminate normal voices from pathological voices.
This paper explores and compares various classification models to find the ability
of acoustic parameters in differentiating normal voices from pathological voices.
An attempt is made to analyze and to discriminate pathological voice from normal
voice in children using different classification methods. The classification of
pathological voice from normal voice is implemented using Support Vector Machine
(SVM) and Radial Basis Functional Neural Network (RBFNN). The normal and pathological
voices of children are used to train and test the classifiers. A dataset is constructed
by recording speech utterances of a set of Tamil phrases. The speech signal is then
analyzed in order to extract the acoustic parameters such as the Signal Energy,
pitch, formant frequencies, Mean Square Residual signal, Reflection coefficients,
Jitter and Shimmer. In this study various acoustic features are combined to form
a feature set, so as to detect voice disorders in children based on which further
treatments can be prescribed by a pathologist. Hence, a successful pathological
voice classification will enable an automatic non-invasive device to diagnose and
analyze the voice of the patient.