This resulted in WER reduction of 0.86%, and 61% of previously out of vocabulary words were correctly recognized.
such a large increase in language model and vocabulary size resulted in the language model perplexity increasing on testing utterances by 647 compared to 498 obtained with language model trained on 22M sentence corpus and using 200K unit vocabulary, so the WER reduction was not as great as anticipated.