Variational Approximation of Long-Span Language Models for LVCSR

Long-span language models that capture syntax and semantics are seldom used in the first
pass of large vocabulary continuous speech recognition systems due to the prohibitive
search-space of sentencehypotheses. Instead, an N-best list of hypotheses is created
using tractable n-gram models, and rescored using the long-span models. It is shown in
this paper that computationally tractable variational approximations of the long-span
models are a better choice than standard n-gram models for first pass decoding. They not
only result in a better first pass output, but also produce a lattice with a lower oracle word
error rate, and rescoring the N-best list from such lattices with the long-span models
requires a smaller N to attain the same accuracy. Empirical results on the WSJ, MIT
Lectures, NIST 2007 Meeting Recognition and NIST 2001 Conversational Telephone
Recognition data sets are presented to support these claims.