Fast LM look-ahead for large vocabulary continuous speech recognition using perfect hashing

In this paper we present a fast method to implement a language model (LM) look-ahead algorithm in a Viterbi-based, single-lexical-tree speech recognizer. We have used three different mechanisms to speed up the calculation: a cache memory attached to each node or the network, a pre-calculation of the probabilities of the active contexts, and an organization of the LM using perfect hash. These enhancements make it possible to use the full trigram LM to compute the look-ahead with better overall results, both in terms of recognition rate and computation time, than using a unigram or bigram based approximation.