Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.

ABSTRACT:

Background

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.

Objective

To address these gaps, we investigated whether interpretable linguistic features can reliably identify AD both within and across language boundaries, focusing on English- and Spanish-speaking patients and healthy controls (HCs).

Methods

We analyzed s

SUBMITTER: Perez-Toro PA 

PROVIDER: S-EPMC12572752 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature