  End-to-end (E2E) automatic speech recognition (ASR) systems often have
difficulty recognizing uncommon words, that appear infrequently in the training
data. One promising method, to improve the recognition accuracy on such rare
words, is to latch onto personalized/contextual information at inference. In
this work, we present a novel context-aware transformer transducer (CATT)
network that improves the state-of-the-art transformer-based ASR system by
taking advantage of such contextual signals. Specifically, we propose a
multi-head attention-based context-biasing network, which is jointly trained
with the rest of the ASR sub-networks. We explore different techniques to
encode contextual data and to create the final attention context vectors. We
also leverage both BLSTM and pretrained BERT based models to encode contextual
data and guide the network training. Using an in-house far-field dataset, we
show that CATT, using a BERT based context encoder, improves the word error
rate of the baseline transformer transducer and outperforms an existing deep
contextual model by 24.2% and 19.4% respectively.
