  Streaming processing of speech audio is required for many contemporary
practical speech recognition tasks. Even with the large corpora of manually
transcribed speech data available today, it is impossible for such corpora to
cover adequately the long tail of linguistic content that's important for tasks
such as open-ended dictation and voice search. We seek to address both the
streaming and the tail recognition challenges by using a language model (LM)
trained on unpaired text data to enhance the end-to-end (E2E) model. We extend
shallow fusion and cold fusion approaches to streaming Recurrent Neural Network
Transducer (RNNT), and also propose two new competitive fusion approaches that
further enhance the RNNT architecture. Our results on multiple languages with
varying training set sizes show that these fusion methods improve streaming
RNNT performance through introducing extra linguistic features. Cold fusion
works consistently better on streaming RNNT with up to a 8.5% WER improvement.
