  End-to-end (E2E) automatic speech recognition models like Recurrent Neural
Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR
applications like voice assistants. While E2E models are very effective at
learning representation of the training data they are trained on, their
accuracy on unseen domains remains a challenging problem. Additionally, these
models require paired audio and text training data, are computationally
expensive and are difficult to adapt towards the fast evolving nature of
conversational speech. In this work, we explore a contextual biasing approach
using likelihood-ratio that leverages text data sources to adapt RNN-T model to
new domains and entities. We show that this method is effective in improving
rare words recognition, and results in a relative improvement of 10% in 1-best
word error rate (WER) and 10% in n-best Oracle WER (n=8) on multiple
out-of-domain datasets without any degradation on a general dataset. We also
show that complementing the contextual biasing adaptation with adaptation of a
second-pass rescoring model gives additive WER improvements.
