  End-to-end speech translation aims to translate speech in one language into
text in another language via an end-to-end way. Most existing methods employ an
encoder-decoder structure with a single encoder to learn acoustic
representation and semantic information simultaneously, which ignores the
speech-and-text modality differences and makes the encoder overloaded, leading
to great difficulty in learning such a model. To address these issues, we
propose a Speech-to-Text Adaptation for Speech Translation (STAST) model which
aims to improve the end-to-end model performance by bridging the modality gap
between speech and text. Specifically, we decouple the speech translation
encoder into three parts and introduce a shrink mechanism to match the length
of speech representation with that of the corresponding text transcription. To
obtain better semantic representation, we completely integrate a text-based
translation model into the STAST so that two tasks can be trained in the same
latent space. Furthermore, we introduce a cross-modal adaptation method to
close the distance between speech and text representation. Experimental results
on English-French and English-German speech translation corpora have shown that
our model significantly outperforms strong baselines, and achieves the new
state-of-the-art performance.
