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End-to-End Automatic Speech Translation of Audiobooks

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 نشر من قبل Alexandre B\\'erard
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
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We investigate end-to-end speech-to-text translation on a corpus of audiobooks specifically augmented for this task. Previous works investigated the extreme case where source language transcription is not available during learning nor decoding, but we also study a midway case where source language transcription is available at training time only. In this case, a single model is trained to decode source speech into target text in a single pass. Experimental results show that it is possible to train compact and efficient end-to-end speech translation models in this setup. We also distribute the corpus and hope that our speech translation baseline on this corpus will be challenged in the future.



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