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
https://github.com/dqqcasia/st.

COSTT 是一种整体方法，可以将语音转文本的源转录文本和目标的翻译文本生成在单个解码器中，从而实现语音翻译的模型训练以充分利用大量的平行文本语料库。实验证明，该方法在三个主流数据集上表现优于或不逊于之前的最新方法。