Performance Improvement of Automatic Pathological Voice Quality Assessment Based on Higher-Order Statistics

This thesis presents new parameters based on the HOS (Higher-Order Statistics) analysis to improve the classification performance of a multi-stage pathological voice assessment system. Automatic pathological diagnosis is a field which still demands further investigation mainly due to the difficulty in quantifying or standardizing the speech pathologists’ diagnoses. In recent years, various speech signal processing techniques have been proposed and applied for the voice disorder diagnosis. The objective is to quantitatively measure the degree of deviation of the pathological from the normal voice patterns with some acoustic analyses. And, objective supports of the diagnostics have some advantages to be adopted directly into the everyday life rather easily with less cost. Although most of the previous researches made novel contributions to the automatic detection of voice disorders and to voice quality assessment, their achievements are not easy to be compared with each other due to the lack of