  In this paper, we present an end-to-end automatic speech recognition system,
which successfully employs subword units in a hybrid CTC-Attention based
system. The subword units are obtained by the byte-pair encoding (BPE)
compression algorithm. Compared to using words as modeling units, using
characters or subword units does not suffer from the out-of-vocabulary (OOV)
problem. Furthermore, using subword units further offers a capability in
modeling longer context than using characters. We evaluate different systems
over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention
system obtains 6.8% word error rate (WER) on the test_clean subset without any
dictionary or external language model. This represents a significant
improvement (a 12.8% WER relative reduction) over the character-based hybrid
CTC-Attention system.
