Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation

@article{Jia2022LeveragingUA,
  title={Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation},
  author={Ye Jia and Yifan Ding and Ankur Bapna and Colin Cherry and Yu Zhang and Alexis Conneau and Nobuyuki Morioka},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.13339}
}
End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research. Recent works have demonstrated that the performance of such direct S2ST systems is approaching that of conventional cascade S2ST when trained on comparable datasets. However, in practice, the performance of direct S2ST is bounded by the availability of paired S2ST training data. In this work, we explore multiple approaches for leveraging much more… 

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