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Recent research has shown that attention-based sequence-to-sequence modelssuch as Listen, Attend, and Spell (LAS) yield comparable results tostate-of-the-art ASR systems on various tasks. In this paper, we describe thedevelopment 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 testclean subset of LibriSpeech English data; second on non-native English speech,including both read speech and spontaneous speech, we obtain very competitiveresults compared to a conventional system built with the most updated Kaldirecipe.

Attention-based sequence-to-sequence model for speech recognition:  development of state-of-the-art system on LibriSpeech and its application to  non-native English