  Although n-gram language models (LMs) have been outperformed by the
state-of-the-art neural LMs, they are still widely used in speech recognition
due to its high efficiency in inference. In this paper, we demonstrate that
n-gram LM can be improved by neural LMs through a text generation based data
augmentation method. In contrast to previous approaches, we employ a
large-scale general domain pre-training followed by in-domain fine-tuning
strategy to construct deep Transformer based neural LMs. Large amount of
in-domain text data is generated with the well trained deep Transformer to
construct new n-gram LMs, which are then interpolated with baseline n-gram
systems. Empirical studies on different speech recognition tasks show that the
proposed approach can effectively improve recognition accuracy. In particular,
our proposed approach brings significant relative word error rate reduction up
to 6.0% for domains with limited in-domain data.
