Contextual spelling correction models are an alternative to shallow fusion to
improve automatic speech recognition (ASR) quality given user vocabulary. To
deal with large user vocabularies, most of these models include candidate
retrieval mechanisms, usually based on minimum edit distance between fragments
of ASR hypothesis and user phrases. However, the edit-distance approach is
slow, non-trainable, and may have low recall as it relies only on common
letters. We propose: 1) a novel algorithm for candidate retrieval, based on
misspelled n-gram mappings, which gives up to 90% recall with just the top 10
candidates on Spoken Wikipedia; 2) a non-autoregressive neural model based on
BERT architecture, where the initial transcript and ten candidates are combined
into one input. The experiments on Spoken Wikipedia show 21.4% word error rate
improvement compared to a baseline ASR system.

使用新颖的基于拼写错误 n-gram 映射的候选检索算法和 BERT 架构的非自回归神经模型，相较于基线 ASR 系统，在口语化维基百科上的实验显示，字错误率提高了 21.4％。