N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets
Abstract
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.
Cite
@article{arxiv.2308.02092,
title = {N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets},
author = {Wang Yau Li and Shreekantha Nadig and Karol Chang and Zafarullah Mahmood and Riqiang Wang and Simon Vandieken and Jonas Robertson and Fred Mailhot},
journal= {arXiv preprint arXiv:2308.02092},
year = {2023}
}