Background: Digital biomarkers continue to make headway in the clinic and clinical trials for neurological conditions. Speech is a domain with great promise. Objectives: Using Friedreich ataxia (FRDA) as an exemplar population, we aimed to align objective measures of speech with markers of disease severity, speech related quality of life and subjective judgements of speech using supervised machine learning techniques. Methods: 132 participants with genetically confirmed diagnosis of FRDA were assessed using digital speech tests, disease severity scores (Friedreich Ataxia Rating Scale, FARS) and speech related quality of life ratings over a 10-year period. Speech was analyzed perceptually by expert listeners for intelligibility (ability to be understood) and naturalness (deviance from healthy norm) and acoustically across 344 features. Features were selected and presented into a random forest and a support vector machine classifier in a standard supervised learning setup designed to replicate expert-produced scores. Results: We demonstrate a subset of measures are strongly associated with all four clinical scales. Objective speech data replicated expert assessments of naturalness and intelligibility. These scores represent a lower level of variability than observed between subjective listener ratings. Findings provide evidence there are specific objective markers of speech that change over time and reflect clinical aspects of the disease. Discussion: The use of a large dataset yielded a speech assay capable of accurately approximating expert listener ratings of key clinical aspects of dysarthria severity. Distinct but complementary subsets align with disease severity and speech related quality of life.

Dr. Adam P. Vogel serves as the Chief Science Officer of Redenlab Pty Ltd and received funding from the Friedreich Ataxia Research Association unrelated to the current study; Dr Paul Maruff serves as Chief Innovation Officer of Cogstate Ltd and Redenlab Inc Hannah Reece reports no disclosures; Hannah Carter reports no disclosures; Geneieve Tai reports no disclosures; Dr Benjamin Schultz reports no disclosures; Dr. Louise Corben reports no disclosures; Dr. Martin Delatycki has received funding from the Friedreich Ataxia Research Association unrelated to the current study; Dr Thanasis Tsanas was a paid consultant for Mirador Analytics.

Adam Vogel was supported by a National Health and Medical Research Council, Australia (Fellowship IDs 1082910 and 1135683) and the Australia Research Council (FT220100253). The study was supported at its early stages by the Wellcome Trust through Centre Grant No. 098461/Z/12/Z from the University of Oxford Sleep & Circadian Neuroscience Institute (SCNi). It was also supported by Health Data Research UK, which receives funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), the British Heart Foundation, and the Wellcome Trust. The funders had no role in the study or the decision to submit this work to be considered for publication.

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The study was approved by The University of Melbourne Human Research Ethics Committee (#1750022) and all participants provided written informed consent.

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