  This paper describes the USTC-NEL system to the speech translation task of
the IWSLT Evaluation 2018. The system is a conventional pipeline system which
contains 3 modules: speech recognition, post-processing and machine
translation. We train a group of hybrid-HMM models for our speech recognition,
and for machine translation we train transformer based neural machine
translation models with speech recognition output style text as input.
Experiments conducted on the IWSLT 2018 task indicate that, compared to
baseline system from KIT, our system achieved 14.9 BLEU improvement.
