A New Epsilon Filter for Efficient Composition of Weighted Finite-State Transducers

In this paper we propose a new composition algorithm for weighted finite-states transducers that are more and more used for speech and pattern recognition applications. Composition joins multiple transducers into one. We have implemented an embedded speech based dialog system for steering applications. Therefore regular grammars are very useful, but they may enlarge strongly by determinization. Composition using the sequential or the matching epsilon-filter does not perform optimal without determinization. Our new algorithm combines the advantages of these two epsilon-filters for size reduction. So composition and decoding time can be saved. It can be applied to many current algorithms including on-the-fly ones.