  Automatic Speech Recognition (ASR) systems have been evolving quickly and
reaching human parity in certain cases. The systems usually perform pretty well
on reading style and clean speech, however, most of the available systems
suffer from situation where the speaking style is conversation and in noisy
environments. It is not straight-forward to tackle such problems due to
difficulties in data collection for both speech and text. In this paper, we
attempt to mitigate the problems using language models combination techniques
that allows us to utilize both large amount of writing style text and small
number of conversation text data. Evaluation on the VLSP 2019 ASR challenges
showed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER on
the VLSP 2019 data sets.
