  Thousands of individuals need surgical removal of their larynx due to
critical diseases every year and therefore, require an alternative form of
communication to articulate speech sounds after the loss of their voice box.
This work addresses the articulatory-to-acoustic mapping problem based on
ultrasound (US) tongue images for the development of a silent-speech interface
(SSI) that can provide them with an assistance in their daily interactions. Our
approach targets automatically extracting tongue movement information by
selecting an optimal feature set from US images and mapping these features to
the acoustic space. We use a novel deep learning architecture to map US tongue
images from the US probe placed beneath a subject's chin to formants that we
call, Ultrasound2Formant (U2F) Net. It uses hybrid spatio-temporal 3D
convolutions followed by feature shuffling, for the estimation and tracking of
vowel formants from US images. The formant values are then utilized to
synthesize continuous time-varying vowel trajectories, via Klatt Synthesizer.
Our best model achieves R-squared (R^2) measure of 99.96% for the regression
task. Our network lays the foundation for an SSI as it successfully tracks the
tongue contour automatically as an internal representation without any explicit
annotation.
