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Objective Pathological Voice Quality Assessment Based on HOS Features
Ji-Yeoun LEE Sangbae JEONG Hong-Shik CHOI Minsoo HAHN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2008/12/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Speech and Hearing
pathological voice quality assessment, higher-order statistics, classification and regression tree, GRBAS,
Full Text: PDF(322.5KB)>>
This work proposes new features to improve the pathological voice quality classification performance. They are the means, the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal, grade 1, grade 2, and grade 3 voices classified in the GRBAS scale. The jitter, the shimmer, the harmonic-to-noise ratio (HNR), and the variance of the short-time energy are utilized as the conventional features. The performances are measured by the classification and regression tree (CART) method. Specifically, the CART-based method by utilizing both the conventional features and the HOS-based ones shows its effectiveness in the pathological voice quality measurement, with the classification accuracy of 87.8%.