  We introduce asynchronous dynamic decoder, which adopts an efficient A*
algorithm to incorporate big language models in the one-pass decoding for large
vocabulary continuous speech recognition. Unlike standard one-pass decoding
with on-the-fly composition decoder which might induce a significant
computation overhead, the asynchronous dynamic decoder has a novel design where
it has two fronts, with one performing "exploration" and the other "backfill".
The computation of the two fronts alternates in the decoding process, resulting
in more effective pruning than the standard one-pass decoding with an
on-the-fly composition decoder. Experiments show that the proposed decoder
works notably faster than the standard one-pass decoding with on-the-fly
composition decoder, while the acceleration will be more obvious with the
increment of data complexity.
