Fast search algorithms for continuous speech recognition

The most important component of a state-of-the-art speech recognition system is the decoder, or search engine. Given this importance, it is no surprise that many algorithms have been devised which attempt to increase the efficiency of the search process while maintaining the quality of the recognition hypotheses. In this paper, we present a Viterbi decoder which uses a two-pass fast-match search to efficiently prune away unlikely parts of the search space. This system is compared to a state-of-the-art Viterbi decoder with beam pruning in evaluations on the OGI Alphadigits Corpus. Experimentation reveals that the Viterbi decoder after a first pass fast-match produces a more efficient search when compared to a Viterbi decoder with beam pruning. However, there is significant overhead associated with the first pass of the fast-match search