Automatic Classification of Healthy Subjects and Patients With Essential Vocal Tremor Using Probabilistic Source-Filter Model Based Noise Robust Pitch Estimation

Rao MV, A and Yamini, BK and Ketan, J and Preetie Shetty, A and Pal, PK and Shivashankar, N and Ghosh, PK (2021) Automatic Classification of Healthy Subjects and Patients With Essential Vocal Tremor Using Probabilistic Source-Filter Model Based Noise Robust Pitch Estimation. In: Journal of Voice .

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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. © 2021 The Voice Foundation

Item Type:	Journal Article
Publication:	Journal of Voice
Publisher:	Mosby Inc.
Additional Information:	The copyright of this article belongs to Mosby Inc.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	09 Mar 2021 08:24
Last Modified:	09 Mar 2021 08:24
URI:	http://eprints.iisc.ac.in/id/eprint/68123

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