Open Access

Linear Classifier with Reject Option for the Detection of Vocal Fold Paralysis and Vocal Fold Edema

EURASIP Journal on Advances in Signal Processing20092009:203790

https://doi.org/10.1155/2009/203790

Received: 1 November 2008

Accepted: 30 July 2009

Published: 22 September 2009

Abstract

Two distinct two-class pattern recognition problems are studied, namely, the detection of male subjects who are diagnosed with vocal fold paralysis against male subjects who are diagnosed as normal and the detection of female subjects who are suffering from vocal fold edema against female subjects who do not suffer from any voice pathology. To do so, utterances of the sustained vowel "ah" are employed from the Massachusetts Eye and Ear Infirmary database of disordered speech. Linear prediction coefficients extracted from the aforementioned utterances are used as features. The receiver operating characteristic curve of the linear classifier, that stems from the Bayes classifier when Gaussian class conditional probability density functions with equal covariance matrices are assumed, is derived. The optimal operating point of the linear classifier is specified with and without reject option. First results using utterances of the "rainbow passage" are also reported for completeness. The reject option is shown to yield statistically significant improvements in the accuracy of detecting the voice pathologies under study.

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Authors’ Affiliations

(1)
Department of Informatics, Aristotle University of Thessaloniki
(2)
Department of Electrical and Computer Engineering, University of Delaware

Copyright

© C. Kotropoulos and G.R. Arce. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.