A tail-sharing WFST composition algorithm for large vocabulary speech recognition

This paper presents an algorithm for approximating minimization in the context of the weighted finite-state transducers approach to large vocabulary speech recognition. The algorithm is designed for the integration of the lexicon with the language model and performs composition, determinization and pushing in one step. Furthermore, it uses tail-sharing in order to approximate minimization. Our results show that it is a good approximation to explicit minimization, with the added advantage that it can be used "on-the-fly" in a dynamic decoder.