Nowadays, most methods in end-to-end contextual speech recognition bias the
recognition process towards contextual knowledge. Since all-neural contextual
biasing methods rely on phrase-level contextual modeling and attention-based
relevance modeling, they may encounter confusion between similar
context-specific phrases, which hurts predictions at the token level. In this
work, we focus on mitigating confusion problems with fine-grained contextual
knowledge selection (FineCoS). In FineCoS, we introduce fine-grained knowledge
to reduce the uncertainty of token predictions. Specifically, we first apply
phrase selection to narrow the range of phrase candidates, and then conduct
token attention on the tokens in the selected phrase candidates. Moreover, we
re-normalize the attention weights of most relevant phrases in inference to
obtain more focused phrase-level contextual representations, and inject
position information to better discriminate phrases or tokens. On LibriSpeech
and an in-house 160,000-hour dataset, we explore the proposed methods based on
a controllable all-neural biasing method, collaborative decoding (ColDec). The
proposed methods provide at most 6.1% relative word error rate reduction on
LibriSpeech and 16.4% relative character error rate reduction on the in-house
dataset over ColDec.

本篇论文主要探讨利用细粒度上下文知识选择方式，以降低基于 “专有” 短语的上下文建模产生的不确定性问题，并引入位置信息来更好地区分短语或令牌，达到在语音识别上提高识别准确率的效果。