Towards the optimal minimization of a pronunciation dictionary model

Abstract

This paper presents the results of our efforts to obtain the minimum possible finite-state representation of a pronunciation dictionary. Finite-state transducers are widely used to encode word pronunciations and our experiments revealed that the conventional redundancy-reduction algorithms developed within this framework yield suboptimal solutions. We found that the incremental construction and redundancy reduction of acyclic finite-state transducers creates considerably smaller models (up to 60%) than the conventional, non-incremental (batch) algorithms implemented in the OpenFST toolkit. © 2010 Springer-Verlag Berlin Heidelberg.