Electromagnetic Articulography (EMA) technique is used
to record the kinematics of different articulators while
one speaks. EMA data often contains missing segments due to
sensor failure. In this work, we propose a maximum
a-posteriori (MAP) estimation with continuity constraint to
recover the missing samples in the articulatory
trajectories recorded using EMA. In this approach, we
combine the benefits of statistical MAP estimation as well
as the temporal continuity of the articulatory
trajectories.