  Recurrent Neural Network Transducer (RNN-T), like most end-to-end speech
recognition model architectures, has an implicit neural network language model
(NNLM) and cannot easily leverage unpaired text data during training. Previous
work has proposed various fusion methods to incorporate external NNLMs into
end-to-end ASR to address this weakness. In this paper, we propose extensions
to these techniques that allow RNN-T to exploit external NNLMs during both
training and inference time, resulting in 13-18% relative Word Error Rate
improvement on Librispeech compared to strong baselines. Furthermore, our
methods do not incur extra algorithmic latency and allow for flexible
plug-and-play of different NNLMs without re-training. We also share in-depth
analysis to better understand the benefits of the different NNLM fusion
methods. Our work provides a reliable technique for leveraging unpaired text
data to significantly improve RNN-T while keeping the system streamable,
flexible, and lightweight.
