Learning non-parametric models of pronunciation

As more data becomes available for a given speech recognition task, the natural way to improve recognition accuracy is to train larger models. But, while this strategy yields modest improvements to small systems, the relative gains diminish as the data and models grow. In this paper, we demonstrate that abundant data allows us to model patterns and structure that are unaccounted for in standard systems. In particular, we model the systematic mismatch between the canonical pronunciations of words and the actual pronunciations found in casual or accented speech. Using a combination of two simple data-driven pronunciation models, we can correct 5.2% of the errors in our mobile voice search application.