Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Weiran Wang Zelin Wu Diamantino Caseiro Tsendsuren Munkhdalai Khe Chai Sim Pat Rondon Golan Pundak Gan Song Rohit Prabhavalkar Zhong Meng Ding Zhao Tara Sainath Pedro Moreno Mengibar
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.