Voice Pathology Classification Using Machine Learning
Özet
In this study, the diagnosis of voice diseases
affecting the sound quality of many people during their life
has been examined. Systems developed for the automatic
classification of healthy and pathological sounds are of great
interest in early detection of voice disorders. The main
purpose of this study is to investigate and compare the
performance of these techniques using various machine
learning techniques for voice pathology. All analyzes are
performed using the Saarbruecken Voice Database. Detection
of voice pathology are evaluated in terms of accuracy,
sensitivity and specificity. Depending on the characteristics
evaluated, the best accuracy is determined as 85.91% by using
SVM algorithm.
Sürdürülebilir Kalkınma Amaçları
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SKA 16: Barış, Adalet ve Güçlü Kurumlar
Atıf için künye
Mohammed, H. M. A., Polat, A. N., Polat, M., Oral, E. A., & Özbek, İ. Y. (2021). Voice Pathology Classification Using Machine Learning. 2nd International Symposium on Applied Science and Engineering (ISASE-2021), Erzurum (ss. 354–357).
BibTeX
@inproceedings{mohammed2021-181083,
author = {Mohammed, Hussein M. A. and Polat, Aslı Nur and Polat, Merve and Oral, Emin Argun and Özbek, İbrahim Yücel},
title = {Voice Pathology Classification Using Machine Learning},
booktitle = {2nd International Symposium on Applied Science and Engineering (ISASE-2021)},
address = {Erzurum},
year = {2021},
pages = {354--357},
url = {https://akademik.atauni.edu.tr/yayinlar/181083-voice-pathology-classification-using-machine-learning}
}