  This work addresses the mismatch problem between the distribution of training
data (source) and testing data (target), in the challenging context of
dysarthric speech recognition. We focus on Speaker Adaptation (SA) in command
speech recognition, where data from multiple sources (i.e., multiple speakers)
are available. Specifically, we propose an unsupervised Multi-Source Domain
Adaptation (MSDA) algorithm based on optimal-transport, called MSDA via
Weighted Joint Optimal Transport (MSDA-WJDOT). We achieve a Command Error Rate
relative reduction of 16% and 7% over the speaker-independent model and the
best competitor method, respectively. The strength of the proposed approach is
that, differently from any other existing SA method, it offers an interpretable
model that can also be exploited, in this context, to diagnose dysarthria
without any specific training. Indeed, it provides a closeness measure between
the target and the source speakers, reflecting their similarity in terms of
speech characteristics. Based on the similarity between the target speaker and
the healthy/dysarthric source speakers, we then define the healthy/dysarthric
score of the target speaker that we leverage to perform dysarthria detection.
This approach does not require any additional training and achieves a 95%
accuracy in the dysarthria diagnosis.
