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

Linguistic Search Optimization for Deep Learning Based LVCSR