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Automatic Speech Recognition (ASR) systems have been evolving quickly andreaching human parity in certain cases. The systems usually perform pretty wellon reading style and clean speech, however, most of the available systemssuffer from situation where the speaking style is conversation and in noisyenvironments. It is not straight-forward to tackle such problems due todifficulties in data collection for both speech and text. In this paper, weattempt to mitigate the problems using language models combination techniquesthat allows us to utilize both large amount of writing style text and smallnumber of conversation text data. Evaluation on the VLSP 2019 ASR challengesshowed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER onthe VLSP 2019 data sets.

VAIS ASR: Building a conversational speech recognition system using language model combination