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.

研究了在韩语语音识别中使用外部语言模型的beam-search解码方法，提出了一种新的韩语标记方法，即在韩语音节中无尾辅音时插入特殊标记SkipTC，实验证明采用SkipTC标记可以使输入语言模型序列更规则化，能更好地学习韩语的语言特征，减少词误率。

语音识别中基于 Beam 搜索重新评分的韩语切词