Total: 1
We propose a GPU/TPU-friendly implementation for contextual biasing based on the Knuth-Morris-Pratt (KMP) pattern matching algorithm. Our algorithms simulate classical search-based biasing approaches which are often implemented in the weighted finite state transducer (WFST) framework, with careful considerations on memory footprint and efficiency by vectorization. We design scoring mechanisms such that, during beam search, a token extension receives a bonus if it extends matching into a biasing phrase, and receives a penalty to cancel previously received bonus otherwise. Our methods could be incorporated in either the shallow fusion or on-the-fly rescoring manner, to trade off accuracy with efficiency. On a large-scale voice search dataset, our method achieves significant word error rate (WER) reductions on biasing test sets without introducing additional model parameters, and yields further performance gain when combined with a model-based biasing method.