  End-to-end (E2E) automatic speech recognition (ASR) systems lack the distinct
language model (LM) component that characterizes traditional speech systems.
While this simplifies the model architecture, it complicates the task of
incorporating text-only data into training, which is important to the
recognition of tail words that do not occur often in audio-text pairs. While
shallow fusion has been proposed as a method for incorporating a pre-trained LM
into an E2E model at inference time, it has not yet been explored for very
large text corpora, and it has been shown to be very sensitive to
hyperparameter settings in the beam search. In this work, we apply shallow
fusion to incorporate a very large text corpus into a state-of-the-art E2EASR
model. We explore the impact of model size and show that intelligent pruning of
the training set can be more effective than increasing the parameter count.
Additionally, we show that incorporating the LM in minimum word error rate
(MWER) fine tuning makes shallow fusion far less dependent on optimal
hyperparameter settings, reducing the difficulty of that tuning problem.
