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Story\u2010tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow\u2010up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow\u2010up period of 5\u2009years.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 180 patients free of parkinsonism or dementia, 156 provided follow\u2010up information. After a mean follow\u2010up of 2.7\u2009years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR\u2009=\u20092.35]) and acoustic abnormalities (HR\u2009=\u20091.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR\u2009=\u20091.74), slower articulation rate (HR\u2009=\u20091.58), and prolonged pauses (HR\u2009=\u20091.46). Dementia\u2010first (n\u2009=\u200916) and parkinsonism\u2010first with mild cognitive impairment (n\u2009=\u20099) converters had higher severity of linguistic abnormalities than parkinsonism\u2010first with normal cognition converters (n\u2009=\u200917).<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. 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