Classification of Huntington Disease using Acoustic and Lexical Features
This work addresses the need for cheap, continuous tracking of HD progression outside clinical settings, though it is incremental as it presents first steps towards such a system.
The paper tackled the problem of expensive and insensitive speech analysis for Huntington Disease by developing an automated system that differentiates healthy controls from individuals with HD using acoustic and lexical features, achieving results that support clinical diagnoses.
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.