Contribution to statistical acoustic-to-EMA mapping

The availability of large corpora of parallel acoustic and articulatory data has enabled the use of statistical, data-driven, methods in the context of the speech inversion problem. This paper explores the use of Support Vector Regression for the mapping from acoustic parameters to electromagnetic articulograph trajectories and compares the outcoming results against those presented in other studies on the same problem and dataset.