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Diagnosis of dementia due to Alzheimer’s disease (AD) is a resource‐intensive process, often requiring clinical assessments and neuroimaging which may fall beyond the reach of patients in remote settings. Given the challenges of traditional dementia work‐up, identification of disease‐relevant signatures from low‐cost modalities has attracted interest as a means to improve disease detection. Here, we describe a machine learning strategy to associate acoustic perturbations in voice with dementia status using digital recordings of neuropsychological exams administered in the Framingham Heart Study (FHS).

We used 118 voice recordings from 79 participants to extract jitter, shimmer, and haronics‐to‐nosie ratio (HNR) from the ComParE 2016 dataset. Each of these acoustic perturbation measures are commonly used in the clinical context to characterize pathological voice. For each voice …