Stable dysphonia measures selection for Parkinson speech rehabilitation via diversity regularized ensemble

Abstract

Vocal impairment is a common symptom for the vast majority of Parkinson's disease (PD) subjects. And it needs long term rehabilitation through personalized one-to-one periodic rehabilitation meetings with clinical speech experts. The significant challenge is that there are not enough experts to deliver the in-person treatments that is needed and for many people with PD, it is difficult to visit the experts for monitoring and treatments. Then there is the need for reliable clinical tools to assist the rehabilitation. This study aims to investigate the potential of using sustained vowel phonations towards objectively and automatically replicating the speech experts' assessments of PD subjects' voices as "acceptable" (a clinician would allow persisting during in-person rehabilitation treatment) or "unacceptable" (a clinician would not allow persisting during in-person rehabilitation treatment). The phonation is usually characterized by many dysphonia measures, which are extracted by clinical speech signal processing algorithms. For this aim, we need to select a stable dys-phonia measures subset, and adopt it to automatically distinguish the PD subjects' voices (acceptable versus unacceptable). In this paper, a diversity regularized ensemble feature weighting algorithm DREFW is presented to choose the stable dysphonia measures subset. The experimental results on real speech rehabilitation data set have shown the proposed algorithm can obtain high stability and classification performance for speech assessment. The findings of this paper is a first step towards improving the effectiveness of an automated rehabilitative speech assessment tool.