Articulatory measurements have been used in a variety of speech science and technology applications. These measurements can be obtained with a number of technologies, such as electromagnetic articulography and X-ray microbeam, typically involving pellets attached to individual articulators. Due to limitations in the recording technologies, articulatory measurements often contain missing data when individual pellets are mis-tracked, leading to relatively high rates of loss in this expensive and time-consuming data source. We present an approach to reconstructing such data, using low-rank matrix factorization techniques combined with temporal smoothness regularization, and apply it to reconstructing the missing entries in the Wisconsin X-ray microbeam database. Our algorithm alternates between two simple steps, each having a closed form as the solution of a linear system. The algorithm gives realistic reconstructions even when a majority of the frames contain missing data, improving over previous approaches to this problem in terms of both root mean squared error and phonetic recognition performance when using the reconstructions.
展开▼
