  The performance of automatic speech recognition (ASR) models can be greatly
improved by proper beam-search decoding with external language model (LM).
There has been an increasing interest in Korean speech recognition, but not
many studies have been focused on the decoding procedure. In this paper, we
propose a Korean tokenization method for neural network-based LM used for
Korean ASR. Although the common approach is to use the same tokenization method
for external LM as the ASR model, we show that it may not be the best choice
for Korean. We propose a new tokenization method that inserts a special token,
SkipTC, when there is no trailing consonant in a Korean syllable. By utilizing
the proposed SkipTC token, the input sequence for LM becomes very regularly
patterned so that the LM can better learn the linguistic characteristics. Our
experiments show that the proposed approach achieves a lower word error rate
compared to the same LM model without SkipTC. In addition, we are the first to
report the ASR performance for the recently introduced large-scale 7,600h
Korean speech dataset.
