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.
@article{arxiv.1802.04200,
title = {End-to-End Automatic Speech Translation of Audiobooks},
author = {Alexandre Bérard and Laurent Besacier and Ali Can Kocabiyikoglu and Olivier Pietquin},
journal= {arXiv preprint arXiv:1802.04200},
year = {2018}
}