Publication Type : Journal Article
Publisher : IJCA
Source : International journal of Computer Applications(IJCA) (2012)
Url : https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.258.9411&rep=rep1&type=pdf
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Verified : Yes
Year : 2012
Abstract : Pathology is the study and diagnosis of disease. Due to the
nature of job, unhealthy habits and voice abuse, the people are
subjected to the risk of voice problems. The diagnosis of
vocal and voice disorders should be in the early stage
otherwise it causes changes in the normal signal. It is well
known that most of vocal fold pathologies cause changes in
the acoustic voice signal. Therefore, the voice signal can be a
useful tool to diagnose them. Acoustic voice analysis can be
used to characterize the pathological voices. This paper
presents the detection of vocal fold pathology with the aid of
the speech signal recorded from the patients. We are going to
recognize the disordered voice for vocal fold disease by
focusing on the classification of pathological voice from
healthy voice based on acoustic features. The method includes
two steps. The first step is the extraction of feature vectors
based on MFCC. The second is the classification of feature
vectors using GMM. The extracted acoustic parameters from
the voice signals are used as an input for the MFCC. The main
advantage of this method is less computation time and
possibility of real-time system development. This report
introduces the design and implementation of the proposed
system for recognizing pathological and normal voice. Also a
description is given about the literature survey done and the
implementation of different modules in the system. The result
of the proposed system and the scope of improvements are
also discussed in the report
Cite this Research Publication : D. .Pravena, Durgadevi, A., and Dhivya, S., “A pathological voice recognition for vocal fold disease”, International journal of Computer Applications(IJCA), 2012.