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arXiv · 2211.04508

SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations

Paul-Ambroise Duquenne·Hongyu Gong·Ning Dong·Jingfei Du·Ann Lee·Vedanuj Goswani·Changhan Wang·Juan Pino·Benoît Sagot·Holger Schwenk
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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 are freely available.

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Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong, Jingfei Du, Ann Lee, Vedanuj Goswani, Changhan Wang, Juan Pino, Benoît Sagot, Holger Schwenk. 2022-11-08. SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations. https://arxiv.org/abs/2211.04508

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