Skip to Main content Skip to Navigation
Conference papers

End-to-End Automatic Speech Translation of Audiobooks

Alexandre Bérard 1, 2, 3 Laurent Besacier 2, 3, 1 Ali Can Kocabiyikoglu 1, 2 Olivier Pietquin 4, 5
5 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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.
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01709586
Contributor : Laurent Besacier <>
Submitted on : Thursday, February 15, 2018 - 10:20:33 AM
Last modification on : Monday, April 20, 2020 - 10:40:03 AM
Document(s) archivé(s) le : Sunday, May 6, 2018 - 4:30:10 AM

File

main.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01709586, version 1

Citation

Alexandre Bérard, Laurent Besacier, Ali Can Kocabiyikoglu, Olivier Pietquin. End-to-End Automatic Speech Translation of Audiobooks. ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada. ⟨hal-01709586⟩

Share

Metrics

Record views

532

Files downloads

885