Contextual biasing refers to the problem of biasing the automatic speech
recognition (ASR) systems towards rare entities that are relevant to the
specific user or application scenarios. We propose algorithms for contextual
biasing based on the Knuth-Morris-Pratt algorithm for pattern matching. During
beam search, we boost the score of a token extension if it extends matching
into a set of biasing phrases. Our method simulates the classical approaches
often implemented in the weighted finite state transducer (WFST) framework, but
avoids the FST language altogether, with careful considerations on memory
footprint and efficiency on tensor processing units (TPUs) by vectorization.
Without introducing additional model parameters, our method achieves
significant word error rate (WER) reductions on biasing test sets by itself,
and yields further performance gain when combined with a model-based biasing
method.

基于 Knuth-Morris-Pratt 算法的上下文偏置方法通过在束搜索期间提升匹配到一组偏置短语的令牌扩展的得分来解决偏向稀有实体的自动语音识别系统的问题，在不引入额外模型参数的情况下，本方法自身在偏置测试集上实现了显著的词错误率的降低，并在与基于模型的偏置方法相结合时获得了进一步的性能提升。