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.

翻译：亨廷顿疾病(HD)是一种渐进性障碍,经常表现在运动机能障碍中。机动车强度(通过运动得分捕获)是评估整个HD严重程度的一个关键组成部分。然而,运动分评价涉及由训练有素的医学专业人员进行临床访问,这种访问费用昂贵,而且并非总可以获得。言语分析为跟踪HD严重程度提供了一个有吸引力的渠道,因为语言易于远程收集,并能对运动变化提供洞察力。HD言语通常被描述为有不规则的连接。在这种思想中,能够捕捉声道运动运动和动脉道协调的声频特征对于确定HD的动脉冲进展特别有希望。在本文中,我们介绍了一项实验,利用从读话中提取的Vocal Tract 协调(VTC) 特征来估计运动分数。在使用弹性-网回归模型时,我们发现VTC 具有显著超出其他声学特征的吸引力,这突出这些特征对短期和长期阅读任务的有效性。最后,我们分析了VTC 与机动车分最相关的可视化的VTC 特征的F值分数。这项工作使得未来研究能够跟踪分析动态目标的磁学分析。