  In this paper, we present Adaptive Computation Steps (ACS) algo-rithm, which
enables end-to-end speech recognition models to dy-namically decide how many
frames should be processed to predict a linguistic output. The model that
applies ACS algorithm follows the encoder-decoder framework, while unlike the
attention-based mod-els, it produces alignments independently at the encoder
side using the correlation between adjacent frames. Thus, predictions can be
made as soon as sufficient acoustic information is received, which makes the
model applicable in online cases. Besides, a small change is made to the
decoding stage of the encoder-decoder framework, which allows the prediction to
exploit bidirectional contexts. We verify the ACS algorithm on a Mandarin
speech corpus AIShell-1, and it achieves a 31.2% CER in the online occasion,
compared to the 32.4% CER of the attention-based model. To fully demonstrate
the advantage of ACS algorithm, offline experiments are conducted, in which our
ACS model achieves an 18.7% CER, outperforming the attention-based counterpart
with the CER of 22.0%.
