Lattice-based search strategies for large vocabulary speech recognition

The design of search algorithms is an important issue in large vocabulary speech recognition, especially as more complex models are developed for improving recognition accuracy. Multi-pass search strategies have been used as a means of applying simple models early on to prune the search space for subsequent passes using more expensive knowledge sources. The pruned search space is typically represented by an N-best sentence list or a word lattice. Here, we investigate three alternatives for lattice search: N-best rescoring, a lattice dynamic programming search algorithm and a lattice local search algorithm. Both the lattice dynamic programming and lattice local search algorithms are shown to achieve comparable performance to the N-best search algorithm while running as much as 10 times faster on a 20 k word lexicon; the local search algorithm has the additional advantage of accommodating sentence-level knowledge sources