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   Vol.E91-D   No.12   pp.2888-2891
Publication Date: 2008/12/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.12.2888
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Speech and Hearing
pathological voice quality assessment,  higher-order statistics,  classification and regression tree,  GRBAS,  

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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%.