  All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that
use a single neural network to transduce audio to word sequences have been
shown to achieve state-of-the-art results on several tasks. In this work, we
examine the ability of E2E models to generalize to unseen domains, where we
find that models trained on short utterances fail to generalize to long-form
speech. We propose two complementary solutions to address this: training on
diverse acoustic data, and LSTM state manipulation to simulate long-form audio
when training using short utterances. On a synthesized long-form test set,
adding data diversity improves word error rate (WER) by 90% relative, while
simulating long-form training improves it by 67% relative, though the
combination doesn't improve over data diversity alone. On a real long-form
call-center test set, adding data diversity improves WER by 40% relative.
Simulating long-form training on top of data diversity improves performance by
an additional 27% relative.
