Fast search strategy in a large vocabulary word recognizer

Abstract

In this article, a fast search algorithm is presented for generating word hypotheses for a 75 000-word vocabulary, speaker-trained, isolated word recognizer. The algorithm is envisioned as the first pass of a total recognition system generating a small number of hypotheses with rough likelihood estimates, to be followed by more detailed hypothesis evaluation. The possible word choices are restricted by estimating the number of syllables in the unknown word using a hidden Markov model (HMM) for syllables. A heuristic search algorithm then searches through a sequence of syllable networks to find the most likely word candidates. Arcs in the syllable network correspond to phonemes. The assumption that the likelihoods of these phoneme arcs are independent of the phonetic context allows us to convert the search through a large tree into a search through a much smaller network or graph. The computational requirements are reduced by roughly a factor of 70 compared to estimating the exact likelihood scores for the 75 000 words. This fast search algorithm is called the syllabic graph search. The recognition accuracy obtained for the syllabic graph search approaches that obtained using the exact likelihood scores for the phoneme sequences.