4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

A Non-linear Filtering Approach to Stochastic Training of the Articulatory-acoustic Mapping Using the EM Algorithm

Gordon Ramsay

Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada

Current techniques for training representations of the articulatory-acoustic mapping from data rely on artificial simulations to provide codebooks of articulatory and acoustic measurements, which are then modelled by simple functional approximations. This paper outlines a stochastic framework for adapting an artificial model to real speech from acoustic measurements alone, using the EM algorithm. It is shown that parameter and state estimation problems for articulatory-acoustic inversion can be solved by adopting a statistical approach based on non-linear filtering.

Full Paper

Bibliographic reference.  Ramsay, Gordon (1996): "A non-linear filtering approach to stochastic training of the articulatory-acoustic mapping using the EM algorithm", In ICSLP-1996, 514-517.