A multitask learning perspective on acoustic-articulatory inversion
This paper proposes the idea that by viewing an inversion mapping MLP from a Multitask Learning perspective, we may be able to relax two constraints which are inherent in using electromagnetic articulography as a source of articulatory information for speech technology purposes. As a first step to evaluating this idea, we perform an inversion mapping experiment in an attempt to ascertain whether the hidden layer of a ``multitask'' MLP can act beneficially as a hidden representation that is shared between inversion mapping subtasks for multiple articulatory targets. Our results in the case of the tongue dorsum x-coordinate indicate this is indeed the case and show good promise. Results for the tongue dorsum y-coordinate however are not so clear-cut, and will require further investigation.