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.
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.
Research Areas
