  We investigate training end-to-end speech recognition models with the
recurrent neural network transducer (RNN-T): a streaming, all-neural,
sequence-to-sequence architecture which jointly learns acoustic and language
model components from transcribed acoustic data. We explore various model
architectures and demonstrate how the model can be improved further if
additional text or pronunciation data are available. The model consists of an
`encoder', which is initialized from a connectionist temporal
classification-based (CTC) acoustic model, and a `decoder' which is partially
initialized from a recurrent neural network language model trained on text data
alone. The entire neural network is trained with the RNN-T loss and directly
outputs the recognized transcript as a sequence of graphemes, thus performing
end-to-end speech recognition. We find that performance can be improved further
through the use of sub-word units (`wordpieces') which capture longer context
and significantly reduce substitution errors. The best RNN-T system, a
twelve-layer LSTM encoder with a two-layer LSTM decoder trained with 30,000
wordpieces as output targets achieves a word error rate of 8.5\% on
voice-search and 5.2\% on voice-dictation tasks and is comparable to a
state-of-the-art baseline at 8.3\% on voice-search and 5.4\% voice-dictation.
