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Using voice recordings from neuropsychological exams at the Framingham Heart Study, we developed a machine learning framework fusing spectral, prosodic, and sound quality measures early in the training cycle. The model\u2019s area under the receiver operating characteristic curve was 0.832 (\u00b10.034) in differentiating persons with dementia from those who had normal cognition. This offers a data-driven framework for analyzing minimally processed voice recordings for cognitive assessment, highlighting the value of digital technologies in disease detection and intervention.<\/jats:p>","DOI":"10.3233\/jad-230560","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T12:11:10Z","timestamp":1697199070000},"page":"507-514","source":"Crossref","is-referenced-by-count":7,"title":["Fusion of Low-Level Descriptors of Digital Voice Recordings for Dementia Assessment"],"prefix":"10.1177","volume":"96","author":[{"given":"Cody","family":"Karjadi","sequence":"first","affiliation":[{"name":"The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA"},{"name":"Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA"},{"name":"Departments of Anatomy & Neurobiology and Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 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