Disordered Speech Assessment Using Automatic Methods Based on Quantitative Measures

Abstract

Speech quality assessment methods are necessary for evaluating and documenting treatment outcomes of patients suffering from degraded speech due to Parkinson's disease, stroke, or other disease processes. Subjective methods of speech quality assessment are more accurate and more robust than objective methods but are time-consuming and costly. We propose a novel objective measure of speech quality assessment that builds on traditional speech processing techniques such as dynamic time warping (DTW) and the Itakura-Saito (IS) distortion measure. Initial results show that our objective measure correlates well with the more expensive subjective methods.