Detection of vocal fold paralysis and oedema using time-domain features and Probabilistic Neural Network Online publication date: Wed, 21-Jan-2015
by M. Hariharan, M.P. Paulraj, Sazali Yaacob
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 6, No. 1, 2011
Abstract: This paper proposes a feature extraction method based on time-domain energy variation for the detection of vocal fold pathology. In this work, two different vocal fold problems (vocal fold paralysis and edema) are taken for analysis and in either case, a two-class pattern recognition problem is investigated. The normal and pathological speech samples are used from Massachusetts Eye and Ear Infirmary database. Probabilistic Neural Network (PNN) is employed for the classification. The experimental results show that the proposed features give very promising classification accuracy of 90% and can be used to detect the vocal fold paralysis and edema clinically.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com