  Automatic speech quality assessment is an important, transversal task whose
progress is hampered by the scarcity of human annotations, poor generalization
to unseen recording conditions, and a lack of flexibility of existing
approaches. In this work, we tackle these problems with a semi-supervised
learning approach, combining available annotations with programmatically
generated data, and using 3 different optimization criteria together with 5
complementary auxiliary tasks. Our results show that such a semi-supervised
approach can cut the error of existing methods by more than 36%, while
providing additional benefits in terms of reusable features or auxiliary
outputs. Improvement is further corroborated with an out-of-sample test showing
promising generalization capabilities.
