An Investigation of Phone-Based Subword Units for End-to-End Speech Recognition
Weiran Wang, Guangsen Wang, Aadyot Bhatnagar, Yingbo Zhou, Caiming Xiong, Richard Socher
Abstract
Phones and their context-dependent variants have been the standard modeling units for conventional speech recognition systems, while characters and subwords have demonstrated their effectiveness for end-to-end recognition systems.We investigate the use of phone-based subwords, in particular, byte pair encoder (BPE), as modeling units for end-to-end speech recognition.In addition, we also developed multi-level language model-based decoding algorithms based on a pronunciation dictionary.Besides the use of the lexicon, which is easily available, our system avoids the need of additional expert knowledge or processing steps from conventional systems.Experimental results show that phone-based BPEs tend to yield more accurate recognition systems than the character-based counterpart.In addition, further improvement can be obtained with a novel one-pass joint beam search decoder, which efficiently combines phone-and character-based BPE systems.For Switchboard, our phone-based BPE system achieves 6.8%/14.4% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6.3%/13.3%WER.On Fisher + Switchboard, joint decoding leads to 4.9%/9.5% WER, setting new milestones for telephony speech recognition.