  Recent research has shown that attention-based sequence-to-sequence models
such as Listen, Attend, and Spell (LAS) yield comparable results to
state-of-the-art ASR systems on various tasks. In this paper, we describe the
development of such a system and demonstrate its performance on two tasks:
first we achieve a new state-of-the-art word error rate of 3.43% on the test
clean subset of LibriSpeech English data; second on non-native English speech,
including both read speech and spontaneous speech, we obtain very competitive
results compared to a conventional system built with the most updated Kaldi
recipe.
