Abstract
Essential voice tremor (EVT) is a voice disorder resulting from dyscoordination within
the laryngeal musculature. A low-frequency fluctuations of fundamental voice frequency
or the strength of excitation amplitude is the main consequence of the disorder. The
automatic classification of healthy control and EVT is useful tool for the clinicians.
A typical automatic EVT classification involves three steps. The first step is to
compute the pitch contour from the speech. The second step is to compute the features
from the pitch contour, and the final step is to use a classifier to classify the
features into healthy or EVT. It is shown that a high-resolution pitch contour estimated
from the glottal closure instants (GCIs) is useful for EVT classification. The HPRC
estimation can be very poor in the presence of noise. Hence, a probabilistic source
filter model based noise robust GCI detection is used for HPRC estimation. The Empirical
mode decomposition based feature extraction is used followed by a support vector machine
classifier. The EVT classification performance is evaluated using recordings from
45 subjects. The proposed method is found to perform better than the baseline techniques
in eight different additive noise conditions with six SNR levels.
Keywords
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Article info
Publication history
Published online: February 10, 2021
Accepted:
January 13,
2021
Identification
Copyright
© 2021 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
