The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kerja ini mencadangkan ciri baharu untuk meningkatkan prestasi klasifikasi kualiti suara patologi. Ia adalah min, varians dan gangguan bagi statistik peringkat tinggi (HOS) seperti kecondongan dan kurtosis. Ciri berasaskan HOS menunjukkan perbezaan bermakna antara suara biasa, gred 1, gred 2 dan gred 3 yang dikelaskan dalam skala GRBAS. Jitter, shimmer, nisbah harmonik-ke-bunyi (HNR), dan varians tenaga masa pendek digunakan sebagai ciri konvensional. Prestasi diukur dengan kaedah klasifikasi dan pokok regresi (CART). Secara khusus, kaedah berasaskan CART dengan menggunakan kedua-dua ciri konvensional dan yang berasaskan HOS menunjukkan keberkesanannya dalam pengukuran kualiti suara patologi, dengan ketepatan klasifikasi 87.8%.
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Salinan
Ji-Yeoun LEE, Sangbae JEONG, Hong-Shik CHOI, Minsoo HAHN, "Objective Pathological Voice Quality Assessment Based on HOS Features" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 12, pp. 2888-2891, December 2008, doi: 10.1093/ietisy/e91-d.12.2888.
Abstract: 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%.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.12.2888/_p
Salinan
@ARTICLE{e91-d_12_2888,
author={Ji-Yeoun LEE, Sangbae JEONG, Hong-Shik CHOI, Minsoo HAHN, },
journal={IEICE TRANSACTIONS on Information},
title={Objective Pathological Voice Quality Assessment Based on HOS Features},
year={2008},
volume={E91-D},
number={12},
pages={2888-2891},
abstract={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%.},
keywords={},
doi={10.1093/ietisy/e91-d.12.2888},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Objective Pathological Voice Quality Assessment Based on HOS Features
T2 - IEICE TRANSACTIONS on Information
SP - 2888
EP - 2891
AU - Ji-Yeoun LEE
AU - Sangbae JEONG
AU - Hong-Shik CHOI
AU - Minsoo HAHN
PY - 2008
DO - 10.1093/ietisy/e91-d.12.2888
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2008
AB - 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%.
ER -