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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este trabalho propõe novas funcionalidades para melhorar o desempenho da classificação da qualidade vocal patológica. Eles são as médias, as variâncias e as perturbações das estatísticas de ordem superior (HOS), como a assimetria e a curtose. Os recursos baseados em HOS mostram diferenças significativas entre vozes normais, grau 1, grau 2 e grau 3 classificadas na escala GRBAS. O jitter, o shimmer, a relação harmônico-ruído (HNR) e a variação da energia de curto prazo são utilizados como recursos convencionais. Os desempenhos são medidos pelo método de árvore de classificação e regressão (CART). Especificamente, o método baseado em CART, utilizando tanto os recursos convencionais quanto os baseados em HOS, mostra sua eficácia na medição da qualidade patológica da voz, com precisão de classificação de 87.8%.
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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
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@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},}
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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 -