Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data

Yuhao Zhang; Chen Xu; Bojie Hu; Chunliang Zhang; Tong Xiao; Jingbo Zhu

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arXiv · 2212.01778

Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data

Yuhao Zhang·Chen Xu·Bojie Hu·Chunliang Zhang·Tong Xiao·Jingbo Zhu

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Abstract

We present a method for introducing a text encoder into pre-trained end-to-end speech translation systems. It enhances the ability of adapting one modality (i.e., source-language speech) to another (i.e., source-language text). Thus, the speech translation model can learn from both unlabeled and labeled data, especially when the source-language text data is abundant. Beyond this, we present a denoising method to build a robust text encoder that can deal with both normal and noisy text data. Our system sets new state-of-the-arts on the MuST-C En-De, En-Fr, and LibriSpeech En-Fr tasks.

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BibTeX RIS

Yuhao Zhang, Chen Xu, Bojie Hu, Chunliang Zhang, Tong Xiao, Jingbo Zhu. 2022-12-04. Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data. https://arxiv.org/abs/2212.01778

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