  Online Transformer-based automatic speech recognition (ASR) systems have been
extensively studied due to the increasing demand for streaming applications.
Recently proposed Decoder-end Adaptive Computation Steps (DACS) algorithm for
online Transformer ASR was shown to achieve state-of-the-art performance and
outperform other existing methods. However, like any other online approach, the
DACS-based attention heads in each of the Transformer decoder layers operate
independently (or asynchronously) and lead to diverged attending positions.
Since DACS employs a truncation threshold to determine the halting position,
some of the attention weights are cut off untimely and might impact the
stability and precision of decoding. To overcome these issues, here we propose
a head-synchronous (HS) version of the DACS algorithm, where the boundary of
attention is jointly detected by all the DACS heads in each decoder layer. ASR
experiments on Wall Street Journal (WSJ), AIShell-1 and Librispeech show that
the proposed method consistently outperforms vanilla DACS and achieves
state-of-the-art performance. We will also demonstrate that HS-DACS has reduced
decoding cost when compared to vanilla DACS.
