Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech

ArXi:2603.15988v1 Announce Type: cross Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale, and the scarcity of labeled data limits robust objective modeling. To address this, we propose a three-stage framework that leverages unlabeled dysarthric speech and large-scale typical speech datasets to scale