Missing samples estimation in electromagnetic articulography data using equality constrained kalman smoother

Abstract

Electromagnetic articulography (EMA) data provides the movement of sensors attached to different articulators of a subject when the subject is speaking. EMA data often contains missing segments due to sensor failure. In this work, we propose an equality constrained Kalman smoother to estimate the missing samples in the EMA data. We incorporate the dynamics of the articulatory movement for missing samples estimation by considering the EMA data vector as the observations from a linear dynamical system. The proposed approach gives 41% reduction on the root mean square error of the estimates compared to the minimum mean square error estimator which does not utilize the dynamics of the articulatory movement. When compared to the maximum a-posteriori estimation with continuity constraints (MAPC) which incorporates smoothness of the articulatory trajectory during estimation, the proposed approach gives an average performance improvement of 4.8%.