Automated differentiation of non‐fluent and logopenic primary progressive aphasia in Italian speakers using acoustic and linguistic speech measures

Introduction Differentiating the nonfluent/agrammatic and logopenic variants of primary progressive aphasia (PPA; nfvPPA and lvPPA, respectively) remains clinically challenging due to overlapping subtle speech and language impairments. We investigated whether automated connected speech analysis can support differential diagnosis. Methods We analyzed connected speech from 84 Italian speakers with PPA (23 nfvPPA, 23 semantic variant [svPPA], and 38 lvPPA) using a picture‐description task. Prosodic, phonological, and morphosyntactic features were extracted. Machine‐learning classifiers were trained for binary (lvPPA vs. nfvPPA) and multiclass (lvPPA, nfvPPA, svPPA) classification. Explainability analyses identified key features. Results The combination of speech and language features effectively discriminated among PPA variants. Binary classification reached 81.67% accuracy, while multiclass classification reached 63.86% accuracy. Noun rate, local jitter, articulation rate, and total pauses were among the most informative features. Discussion Automated analysis of connected speech provides objective linguistic biomarkers that enhance diagnostic accuracy and support reliable differentiation of PPA variants in clinical practice.