Summary
This study aims to detect voice disorders related to vocal fold nodule, Reinke’s edema
and neurological pathologies through multiband cepstral features of the sustained
vowel /a/. Detection is performed between pairs of study groups and multiband analysis
is accomplished using the wavelet transform. For each pair of groups, a parameters
selection is carried out. Time series of the selected parameters are used as input
for four classifiers with leave-one-out cross validation. Classification accuracies
of 100% are achieved for all pairs including the control group, surpassing the state-of-art
methods based on cepstral features, while accuracies higher than 88.50% are obtained
for the pathological pairs. The results indicated that the method may be adequate
to assist in the diagnosis of the voice disorders addressed. The results must be updated
in the future with a larger population to ensure generalization.
Key Words
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Article info
Publication history
Published online: March 01, 2021
Accepted:
January 21,
2021
Identification
Copyright
© 2021 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
