Exploring Model Units and Training Strategies for End-to-End Speech Recognition

Abstract

In this work, we explore end-to-end speech recognition models (CTC, RNN-Transducer and attention-based models) with different model units (character, wordpiece and word) and various training strategies. We show that wordpiece unit outperforms character unit for all end-to-end systems on the Switchboard Hub5'00 benchmark. To improve the performance of end-to-end systems, we propose a multi-stage pretraining strategy, which gives 25.0% and 18.0% relative improvements over training from scratch for attention and RNN-T models respectively with wordpiece units. We achieve state-of-the-art performance on the Switchboard+Fisher-2000h task, outperforming all prior work. Together with other training strategies such as label smoothing and data augmentation, we achieve 5.9%/12.1% WER on the Switch-board/CallHome test set without using any external language models. This is a new performance milestone for a single end-to-end system, and it is also much better than the previous published best hybrid system, which is 6.7%/12.5% on each set individually.