Matrix fast match: a fast method for identifying a short list of candidate words for decoding

Abstract

A rapid method is presented for identifying a short list of candidate words that match well with some acoustic input to serve as a fast matching stage in a large-vocabulary speech-recognition system that uses hidden Markov models and maximum a posteriori decoding. Given hidden Markov models for all the words in the vocabulary the authors derive a class of algorithms that are faster than a detailed likelihood computation using these models by constructing an estimator of the likelihood. Using such an estimator they produce a list of candidate words that match well with the given acoustic input which has the property that it is guaranteed to contain the correct word in all the cases where a detailed likelihood computation would assign the maximum likelihood to that word.