  Accurate transcription of proper names and technical terms is particularly
important in speech-to-text applications for business conversations. These
words, which are essential to understanding the conversation, are often rare
and therefore likely to be under-represented in text and audio training data,
creating a significant challenge in this domain. We present a two-step keyword
boosting mechanism that successfully works on normalized unigrams and n-grams
rather than just single tokens, which eliminates missing hits issues with
boosting raw targets. In addition, we show how adjusting the boosting weight
logic avoids over-boosting multi-token keywords. This improves our keyword
recognition rate by 26% relative on our proprietary in-domain dataset and 2% on
LibriSpeech. This method is particularly useful on targets that involve
non-alphabetic characters or have non-standard pronunciations.
