Building a diagnosis model for primary progressive aphasia (PPA) has been challenging due to the data scarcity. Collecting clinical data at scale is limited by the high vulnerability of clinical population and the high cost of expert labeling. To circumvent this, previous studies simulate dysfluent speech to generate training data. However, those approaches are not comprehensive enough to simulate PPA as holistic, multi-level phenotypes, instead relying on isolated dysfluencies. To address this, we propose a novel, clinically grounded simulation framework, Hierarchical Aphasic Speech Simulation (HASS). HASS aims to simulate behaviors of logopenic variant of PPA (lvPPA) with varying degrees of severity. To this end, semantic, phonological, and temporal deficits of lvPPA are systematically identified by clinical experts, and simulated. We demonstrate that our framework enables more accurate and generalizable detection models.
@article{arxiv.2603.26795,
title = {HASS: Hierarchical Simulation of Logopenic Aphasic Speech for Scalable PPA Detection},
author = {Harrison Li and Kevin Wang and Cheol Jun Cho and Jiachen Lian and Rabab Rangwala and Chenxu Guo and Emma Yang and Lynn Kurteff and Zoe Ezzes and Willa Keegan-Rodewald and Jet Vonk and Siddarth Ramkrishnan and Giada Antonicelli and Zachary Miller and Marilu Gorno Tempini and Gopala Anumanchipalli},
journal= {arXiv preprint arXiv:2603.26795},
year = {2026}
}