Estimating missing data sequences in X-ray microbeam recordings

Techniques for recording the vocal tract shape during speech such as X-ray microbeam or EMA track the spatial location of pellets attached to several articulators. Limitations of the recording technology result in most utterances having sequences of frames where one or more pellets are missing. Rather than discarding such sequences, we seek to reconstruct them. We use an algorithm for recovering missing data based on learning a density model of the vocal tract shapes, and predicting missing articulator values using conditional distributions derived from this density. Our results with the Wisconsin X-ray microbeam database show we can recover long, heavily oscillatory trajectories with errors of 1 to 1.5 mm for all articulators.