  Simultaneous speech translation (SimulST) translates partial speech inputs
incrementally. Although the monotonic correspondence between input and output
is preferable for smaller latency, it is not the case for distant language
pairs such as English and Japanese. A prospective approach to this problem is
to mimic simultaneous interpretation (SI) using SI data to train a SimulST
model. However, the size of such SI data is limited, so the SI data should be
used together with ordinary bilingual data whose translations are given in
offline. In this paper, we propose an effective way to train a SimulST model
using mixed data of SI and offline. The proposed method trains a single model
using the mixed data with style tags that tell the model to generate SI- or
offline-style outputs. Experiment results show improvements of BLEURT in
different latency ranges, and our analyses revealed the proposed model
generates SI-style outputs more than the baseline.
