N-gram Boosting: Improving Contextual Biasing with Normalized N-gram
  Targets
Wang Yau Li Shreekantha Nadig Karol Chang Zafarullah Mahmood Riqiang Wang Simon Vandieken Jonas Robertson Fred Mailhot
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.