N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets

• URL: http://arxiv.org/abs/2308.02092v1
• Date: Fri, 4 Aug 2023 00:23:14 GMT
• Title: N-gram Boosting: Improving Contextual Biasing with Normalized N-gram Targets
• Authors: Wang Yau Li, Shreekantha Nadig, Karol Chang, Zafarullah Mahmood, Riqiang Wang, Simon Vandieken, Jonas Robertson, Fred Mailhot
• Abstract summary: We present a two-step keyword boosting mechanism that works on normalized unigrams and n-grams rather than just single tokens. This improves our keyword recognition rate by 26% relative on our proprietary in-domain dataset and 2% on LibriSpeech.
• Score: 1.9908600514057855
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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