Author:
Tracey Brian,Volfson Dmitri,Glass James,Haulcy R’mani,Kostrzebski Melissa,Adams Jamie,Kangarloo Tairmae,Brodtmann Amy,Dorsey E. Ray,Vogel Adam
Abstract
AbstractWhile speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.
Funder
Takeda Pharmaceuticals U.S.A.
Biogen
members of the Critical Path for Parkinson’s Consortium 3DT Initiative, Stage 2
Publisher
Springer Science and Business Media LLC
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