  Speech-to-text translation (ST), which directly translates the source
language speech to the target language text, has attracted intensive attention
recently. However, the combination of speech recognition and machine
translation in a single model poses a heavy burden on the direct cross-modal
cross-lingual mapping. To reduce the learning difficulty, we propose
COnSecutive Transcription and Translation (COSTT), an integral approach for
speech-to-text translation. The key idea is to generate source transcript and
target translation text with a single decoder. It benefits the model training
so that additional large parallel text corpus can be fully exploited to enhance
the speech translation training. Our method is verified on three mainstream
datasets, including Augmented LibriSpeech English-French dataset, IWSLT2018
English-German dataset, and TED English-Chinese dataset. Experiments show that
our proposed COSTT outperforms or on par with the previous state-of-the-art
methods on the three datasets. We have released our code at
\url{https://github.com/dqqcasia/st}.
