  Recently, speech-to-text translation has attracted more and more attention
and many studies have emerged rapidly. In this paper, we present a
comprehensive survey on direct speech translation aiming to summarize the
current state-of-the-art techniques. First, we categorize the existing research
work into three directions based on the main challenges -- modeling burden,
data scarcity, and application issues. To tackle the problem of modeling
burden, two main structures have been proposed, encoder-decoder framework
(Transformer and the variants) and multitask frameworks. For the challenge of
data scarcity, recent work resorts to many sophisticated techniques, such as
data augmentation, pre-training, knowledge distillation, and multilingual
modeling. We analyze and summarize the application issues, which include
real-time, segmentation, named entity, gender bias, and code-switching.
Finally, we discuss some promising directions for future work.
