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Automated speech and language analysis (ASLA) is gaining momentum as a noninvasive, affordable, and scalable approach for the early detection of Alzheimer disease (AD). Nevertheless, the literature presents 2 notable limitations. First, many studies use computationally derived features that lack clinical interpretability. Second, a significant proportion of ASLA studies have been conducted exclusively in English speakers. These shortcomings reduce the utility and generalizability of existing findings.

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