  Most speech-to-text (S2T) translation studies use English speech as a source,
which makes it difficult for non-English speakers to take advantage of the S2T
technologies. For some languages, this problem was tackled through corpus
construction, but the farther linguistically from English or the more
under-resourced, this deficiency and underrepresentedness becomes more
significant. In this paper, we introduce kosp2e (read as `kospi'), a corpus
that allows Korean speech to be translated into English text in an end-to-end
manner. We adopt open license speech recognition corpus, translation corpus,
and spoken language corpora to make our dataset freely available to the public,
and check the performance through the pipeline and training-based approaches.
Using pipeline and various end-to-end schemes, we obtain the highest BLEU of
21.3 and 18.0 for each based on the English hypothesis, validating the
feasibility of our data. We plan to supplement annotations for other target
languages through community contributions in the future.
