  This article describes an efficient training method for online streaming
attention-based encoder-decoder (AED) automatic speech recognition (ASR)
systems. AED models have achieved competitive performance in offline scenarios
by jointly optimizing all components. They have recently been extended to an
online streaming framework via models such as monotonic chunkwise attention
(MoChA). However, the elaborate attention calculation process is not robust for
long-form speech utterances. Moreover, the sequence-level training objective
and time-restricted streaming encoder cause a nonnegligible delay in token
emission during inference. To address these problems, we propose CTC
synchronous training (CTC-ST), in which CTC alignments are leveraged as a
reference for token boundaries to enable a MoChA model to learn optimal
monotonic input-output alignments. We formulate a purely end-to-end training
objective to synchronize the boundaries of MoChA to those of CTC. The CTC model
shares an encoder with the MoChA model to enhance the encoder representation.
Moreover, the proposed method provides alignment information learned in the CTC
branch to the attention-based decoder. Therefore, CTC-ST can be regarded as
self-distillation of alignment knowledge from CTC to MoChA. Experimental
evaluations on a variety of benchmark datasets show that the proposed method
significantly reduces recognition errors and emission latency simultaneously,
especially for long-form and noisy speech. We also compare CTC-ST with several
methods that distill alignment knowledge from a hybrid ASR system and show that
the CTC-ST can achieve a comparable tradeoff of accuracy and latency without
relying on external alignment information. The best MoChA system shows
performance comparable to that of RNN-transducer (RNN-T).
