AI

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.