  Large language models have been proven quite beneficial for a variety of
automatic speech recognition tasks in Google. We summarize results on Voice
Search and a few YouTube speech transcription tasks to highlight the impact
that one can expect from increasing both the amount of training data, and the
size of the language model estimated from such data. Depending on the task,
availability and amount of training data used, language model size and amount
of work and care put into integrating them in the lattice rescoring step we
observe reductions in word error rate between 6% and 10% relative, for systems
on a wide range of operating points between 17% and 52% word error rate.
