  In many real-world applications, the mismatch between distributions of
training data (source) and test data (target) significantly degrades the
performance of machine learning algorithms. In speech data, causes of this
mismatch include different acoustic environments or speaker characteristics. In
this paper, we address this issue in the challenging context of dysarthric
speech, by multi-source domain/speaker adaptation (MSDA/MSSA). Specifically, we
propose the use of an optimal-transport based approach, called MSDA via
Weighted Joint Optimal Transport (MSDA-WDJOT). We confront the mismatch problem
in dysarthria detection for which the proposed approach outperforms both the
Baseline and the state-of-the-art MSDA models, improving the detection accuracy
of 0.9% over the best competitor method. We then employ MSDA-WJDOT for
dysarthric speaker adaptation in command speech recognition. This provides a
Command Error Rate relative reduction of 16% and 7% over the baseline and the
best competitor model, respectively. Interestingly, MSDA-WJDOT provides a
similarity score between the source and the target, i.e. between speakers in
this case. We leverage this similarity measure to define a Dysarthric and
Healthy score of the target speaker and diagnose the dysarthria with an
accuracy of 95%.
