Lattice-based Search Strategies for Large Vocabulary Speech
The design of search algorithms is an important issue in recognition,
particularly for very large vocabulary, continuous speech. It is an
especially crucial problem when computationally expensive knowledge sources
are used in the system, as is necessary to achieve high accuracy.
Recently, multi-pass search strategies have been used as a means of
applying inexpensive knowledge sources early on to prune the search space for
subsequent passes using more expensive knowledge sources. Three multi-pass
search algorithms are investigated in this thesis work: the N-best search
algorithm, 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
20,000 word vocabulary task. The lattice local search algorithm is also
shown to have the additional advantage over the lattice dynamic programming
search algorithm of allowing sentence-level knowledge sources to be
incorporated into the search.
The full thesis in postscript format. (874 kB)
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