Abstract
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.
Original language | English (US) |
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Article number | 015201 |
Journal | JASA Express Letters |
Volume | 3 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2023 |
ASJC Scopus subject areas
- Acoustics and Ultrasonics
- Music
- Arts and Humanities (miscellaneous)