| Autor: |
Adam P. Vogel, Paul Maruff, Hannah Reece, Hannah Carter, Geneieve Tai, Benjamin G. Schultz, Louise Corben, Martin B Delatycki, Athanasios Tsanas |
| Rok vydání: |
2023 |
| Popis: |
BackgroundDigital biomarkers continue to make headway in the clinic and clinical trials for neurological conditions. Speech is a domain with great promise.ObjectivesUsing 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.Methods132 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.ResultsWe demonstrate a subset of measures are strongly associated with all four clinical scales. Objective speech data replicated experts’ 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.DiscussionThe 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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