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Speech was continuously captured during phone calls via a standardized smartphone application, segmented, and analyzed for speech impairment severity end points and key acoustic features of monopitch, vowel articulation, voice quality, and articulation rate. We used linear mixed\u2010effect modeling to estimate speech progression and calculated sample size requirements to demonstrate slowing of progression under anticipated treatment effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Over 31,000 phone calls (&gt;1,000\u2009hours) were collected from 71 participants including those with iRBD, PD, and healthy controls. Compared with controls, both individuals with iRBD and PD showed significant declines in speech impairment severity end points based on spectral changes and artificial intelligence\u2010based neural embeddings. The subjects with iRBD also exhibited declines in vowel articulation and articulation rate. For a 2\u2010year neuroprotective trial aiming for 50% drug efficacy, the most efficient sample size estimate based on time\u2010to\u2010event analysis was 74 iRBD and 84 PD participants per arm using the neural embedding end point.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>The phone call analysis requiring no patient effort or clinical supervision can detect speech decline in prodromal and early synucleinopathies, providing a potential paradigm shift for clinical trial design and neuroprotective intervention end points. 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