Automated detection of progressive speech changes in early Alzheimer's disease

Abstract

Abstract Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open‐ended speech samples from a prodromal‐to‐mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech‐based composite scores could be used to monitor change and detect response to treatment in future research. HIGHLIGHTS Longitudinal speech samples were analyzed to characterize speech changes in early AD. Acoustic and linguistic measures showed significant change over 18 months. A novel speech composite score was computed to characterize longitudinal change. The speech composite correlated with primary and secondary trial endpoints. Automated speech analysis could facilitate remote, high frequency monitoring in AD.

Keywords

• Alzheimer's disease
• digital biomarkers
• language
• mild cognitive impairment
• natural language processing
• speech