School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona 85281, USA.
College of Health Solutions, Arizona State University, Tempe, Arizona 85281, USA
JASA Express Lett. 2023 Jan;3(1):015201. doi: 10.1121/10.0016833.
Studies have shown deep neural networks (DNN) as a potential tool for classifying dysarthric speakers and controls. However, representations used to train DNNs are largely not clinically interpretable, which limits clinical value. Here, a model with a bottleneck layer is trained to jointly learn a classification label and four clinically-interpretable features. Evaluation of two dysarthria subtypes shows that the proposed method can flexibly trade-off between improved classification accuracy and discovery of clinically-interpretable deficit patterns. The analysis using Shapley additive explanation shows the model learns a representation consistent with the disturbances that define the two dysarthria subtypes considered in this work.
研究表明，深度神经网络（DNN）是一种用于对构音障碍说话者和对照组进行分类的潜在工具。然而，用于训练 DNN 的表示形式在很大程度上是不可临床解释的，这限制了其临床价值。在这里，训练了一个具有瓶颈层的模型，以联合学习分类标签和四个可临床解释的特征。对两种构音障碍亚型的评估表明，所提出的方法可以灵活地在提高分类准确性和发现可临床解释的缺陷模式之间进行权衡。使用 Shapley 加法解释的分析表明，该模型学习的表示形式与定义本研究中考虑的两种构音障碍亚型的干扰一致。
