  The scarcity of training data and the large speaker variation in dysarthric
speech lead to poor accuracy and poor speaker generalization of spoken language
understanding systems for dysarthric speech. Through work on the speech
features, we focus on improving the model generalization ability with limited
dysarthric data. Factorized Hierarchical Variational Auto-Encoders (FHVAE)
trained unsupervisedly have shown their advantage in disentangling content and
speaker representations. Earlier work showed that the dysarthria shows in both
feature vectors. Here, we add adversarial training to bridge the gap between
the control and dysarthric speech data domains. We extract dysarthric and
speaker invariant features using weak supervision. The extracted features are
evaluated on a Spoken Language Understanding task and yield a higher accuracy
on unseen speakers with more severe dysarthria compared to features from the
basic FHVAE model or plain filterbanks.
