  Recent advances in deep learning based large vocabulary con- tinuous speech
recognition (LVCSR) invoke growing demands in large scale speech transcription.
The inference process of a speech recognizer is to find a sequence of labels
whose corresponding acoustic and language models best match the input feature
[1]. The main computation includes two stages: acoustic model (AM) inference
and linguistic search (weighted finite-state transducer, WFST). Large
computational overheads of both stages hamper the wide application of LVCSR.
Benefit from stronger classifiers, deep learning, and more powerful computing
devices, we propose general ideas and some initial trials to solve these
fundamental problems.
