Related Experiment Video
Updated: Aug 16, 2026
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
Published on: September 27, 2024
Automated differentiation of non-fluent and logopenic primary progressive aphasia in Italian speakers using acoustic
Francesco Pierotti1, Carmen Morinelli2, Valentina Moschini2
1The Biorobotics Institute & the Department of Excellence for Robotics and AI Scuola Superiore Sant'Anna Pisa Italy.
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.
