Bayesian Language Model Interpolation for Mobile Speech Input
Abstract: This paper explores various static interpolation methods for
approximating a single dynamically-interpolated language model used for a variety of
recognition tasks on the Google Android platform. The goal is to ﬁnd the
statically-interpolated ﬁrstpass LM that best reduces search errors in a two-pass
system or that even allows eliminating the more complex dynamic second pass entirely.
Static interpolation weights that are uniform, prior-weighted, and the maximum
likelihood, maximum a posteriori, and Bayesian solutions are considered. Analysis
argues and recognition experiments on Android test data show that a Bayesian
interpolation approach performs best.