  Speech is a critical biomarker for Huntington Disease (HD), with changes in
speech increasing in severity as the disease progresses. Speech analyses are
currently conducted using either transcriptions created manually by trained
professionals or using global rating scales. Manual transcription is both
expensive and time-consuming and global rating scales may lack sufficient
sensitivity and fidelity. Ultimately, what is needed is an unobtrusive measure
that can cheaply and continuously track disease progression. We present first
steps towards the development of such a system, demonstrating the ability to
automatically differentiate between healthy controls and individuals with HD
using speech cues. The results provide evidence that objective analyses can be
used to support clinical diagnoses, moving towards the tracking of
symptomatology outside of laboratory and clinical environments.
