SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations
Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong, Jingfei Du, Ann Lee, Vedanuj Goswami, Changhan Wang, Juan Pino
Abstract
We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models will be publicly released
BibTeX
@inproceedings{duquenne-etal-2023-speechmatrix,
title = "{S}peech{M}atrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations",
author = "Duquenne, Paul-Ambroise and
Gong, Hongyu and
Dong, Ning and
Du, Jingfei and
Lee, Ann and
Goswami, Vedanuj and
Wang, Changhan and
Pino, Juan and
Sagot, Beno{\^i}t and
Schwenk, Holger",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.899/",
doi = "10.18653/v1/2023.acl-long.899",
pages = "16251--16269"
}