  Voice disorders are pathologies significantly affecting patient quality of
life. However, non-invasive automated diagnosis of these pathologies is still
under-explored, due to both a shortage of pathological voice data, and
diversity of the recording types used for the diagnosis. This paper proposes a
novel solution that adopts transformers directly working on raw voice signals
and addresses data shortage through synthetic data generation and data
augmentation. Further, we consider many recording types at the same time, such
as sentence reading and sustained vowel emission, by employing a Mixture of
Expert ensemble to align the predictions on different data types. The
experimental results, obtained on both public and private datasets, show the
effectiveness of our solution in the disorder detection and classification
tasks and largely improve over existing approaches.
