  End-to-end models that condition the output label sequence on all previously
predicted labels have emerged as popular alternatives to conventional systems
for automatic speech recognition (ASR). Since unique label histories correspond
to distinct models states, such models are decoded using an approximate
beam-search process which produces a tree of hypotheses.
  In this work, we study the influence of the amount of label context on the
model's accuracy, and its impact on the efficiency of the decoding process. We
find that we can limit the context of the recurrent neural network transducer
(RNN-T) during training to just four previous word-piece labels, without
degrading word error rate (WER) relative to the full-context baseline. Limiting
context also provides opportunities to improve the efficiency of the
beam-search process during decoding by removing redundant paths from the active
beam, and instead retaining them in the final lattice. This path-merging scheme
can also be applied when decoding the baseline full-context model through an
approximation. Overall, we find that the proposed path-merging scheme is
extremely effective allowing us to improve oracle WERs by up to 36% over the
baseline, while simultaneously reducing the number of model evaluations by up
to 5.3% without any degradation in WER.
