A Pathological Voices Assessment Using Classification

Abstract

The diagnosing of pathological voice may be a tedious topic and it receives abundant attention. There are several diseases that adversely have an effect on our human speech (voice). The doctor will use only the equipments for detection of pathological voice. However, it is invasive and needs an skilled analysis of diverse human speech signal parameters. Automatic voice analysis for pathological speech has its own blessings, like 1) its quantitative and non-invasive nature. 2) permitting the identification and observance of vocal system diseases. Within the pathological voice classification techniques gathered by the voice of a patient, the goal is to discriminate whether the given voice is normal or pathological. From the speech Mel-Frequency Cepstral Coefficients (MFCC) has been extracted from the voice information and classified into two categories. However, the accuracy of the earlier classification methodology may need additional improvement. In my project work, Support Vector Machine (SVM) classifier is used for pathological voice classification with non-invasive nature to diagnose and analyze the voice of the patient.