SESQA: Semi-supervised Learning for Speech Quality Assessment1 min read

Can semi-supervised learning help us estimating accurate MOS quality estimates of speech? Yes, and not only that – its heads used for semi-supervised training can also convey relevant information related to the task.

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Abstract – 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.