AI

A Generalized Composition Algorithm for Weighted Finite-State Transducers

Abstract

This paper describes a weighted finite-state transducer composition algorithm that generalizes the notion of the composition filter and present filters that remove useless epsilon paths and push forward labels and weights along epsilon paths. This filtering allows us to compose together large speech recognition context-dependent lexicons and language models much more efficiently in time and space than previously possible. We present experiments on Broadcast News and Google Search by Voice that demonstrate a 5% to 10% overhead for dynamic, runtime composition compared to a static, offline composition of the recognition transducer. To our knowledge, this is the first such system with such small overhead.