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.
@article{arxiv.1807.04978,
title = {Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units},
author = {Zhangyu Xiao and Zhijian Ou and Wei Chu and Hui Lin},
journal= {arXiv preprint arXiv:1807.04978},
year = {2018}
}