Articulatory Coordination for Speech Motor Tracking in Huntington Disease

TL;DR

This study demonstrates that Vocal Tract Coordination features extracted from speech can effectively estimate Huntington Disease motor severity, outperforming other acoustic features, and offers a remote, accessible method for tracking disease progression.

Contribution

The paper introduces the use of Vocal Tract Coordination features for estimating HD motor severity from speech, showing their superiority over other acoustic features in predictive accuracy.

Findings

VTC features significantly outperform other acoustic features in estimating motor scores.

VTC features are effective across both short and long speech segments.

Analysis of F-value scores reveals key channels related to motor severity.

Abstract

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.