  Direct speech translation (ST) has shown to be a complex task requiring
knowledge transfer from its sub-tasks: automatic speech recognition (ASR) and
machine translation (MT). For MT, one of the most promising techniques to
transfer knowledge is knowledge distillation. In this paper, we compare the
different solutions to distill knowledge in a sequence-to-sequence task like
ST. Moreover, we analyze eventual drawbacks of this approach and how to
alleviate them maintaining the benefits in terms of translation quality.
