  Articulatory-to-acoustic (forward) mapping is a technique to predict speech
using various articulatory acquisition techniques as input (e.g. ultrasound
tongue imaging, MRI, lip video). The advantage of lip video is that it is
easily available and affordable: most modern smartphones have a front camera.
There are already a few solutions for lip-to-speech synthesis, but they mostly
concentrate on offline training and inference. In this paper, we propose a
system built from a backend for deep neural network training and inference and
a fronted as a form of a mobile application. Our initial evaluation shows that
the scenario is feasible: a top-5 classification accuracy of 74% is combined
with feedback from the mobile application user, making sure that the speaking
impaired might be able to communicate with this solution.
