  Huntington Disease (HD) is a progressive disorder which often manifests in
motor impairment. Motor severity (captured via motor score) is a key component
in assessing overall HD severity. However, motor score evaluation involves
in-clinic visits with a trained medical professional, which are expensive and
not always accessible. Speech analysis provides an attractive avenue for
tracking HD severity because speech is easy to collect remotely and provides
insight into motor changes. HD speech is typically characterized as having
irregular articulation. With this in mind, acoustic features that can capture
vocal tract movement and articulatory coordination are particularly promising
for characterizing motor symptom progression in HD. In this paper, we present
an experiment that uses Vocal Tract Coordination (VTC) features extracted from
read speech to estimate a motor score. When using an elastic-net regression
model, we find that VTC features significantly outperform other acoustic
features across varied-length audio segments, which highlights the
effectiveness of these features for both short- and long-form reading tasks.
Lastly, we analyze the F-value scores of VTC features to visualize which
channels are most related to motor score. This work enables future research
efforts to consider VTC features for acoustic analyses which target HD motor
symptomatology tracking.
