A novel voice feature ava and its application to the pathological voice detection through machine learning

Shazlyn Milleana Shaharudin

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Type :	article
Subject :	Q Science (General)
ISSN :	2158-107X
Main Author :	Shazlyn Milleana Shaharudin
Title :	A novel voice feature ava and its application to the pathological voice detection through machine learning

Place of Production :	Tanjung Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2023
Notes :	International Journal of Advanced Computer Science and Applications
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link

Abstract : Universiti Pendidikan Sultan Idris

Voice pathology is a universal problem which must be addressed. Traditionally, this malady is treated by using the surgical instruments in the varied healthcare settings. In the current era, machine learning experts have paid an increasing attention towards the solution of this problem by exploiting the signal processing of the voice. For this purpose, numerous voice features have been capitalized to classify the healthy and pathological voice signals. In particular, Mel-Frequency Cepstral Coefficients (MFCC) is a widely used feature in speech and audio signal processing. It denotes spectral characteristics of a voice signal, particularly of human speech. The modus operandi of MFCC is too time-consuming, which goes against the hasty and urgent nature of the modern times. This study has developed a yet another voice feature by utilizing the average value of the amplitudes (AVA) of the voice signals. Moreover, Gaussian Naive Bayes classifier has been employed to classify the given voice signals as healthy or pathological. Apart from that, the dataset has been acquired from the SVD (Saarbrucken Voice Database) to demonstrate the workability of the proposed voice feature and its usage in the classifier. The machine experimentation rendered very promising results. Particularly, Recall, F1 and accuracy scores obtained, are 100%, 83% and 80%, respectively. These results vividly imply that the proposed classifier can be installed in various healthcare settings. (2023), (Science and Information Organization). All Rights Reserved.

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