  This paper presents XLSR which learns cross-lingual speech representations by
pretraining a single model from the raw waveform of speech in multiple
languages. We build on wav2vec 2.0 which is trained by solving a contrastive
task over masked latent speech representations and jointly learns a
quantization of the latents shared across languages. The resulting model is
fine-tuned on labeled data and experiments show that cross-lingual pretraining
significantly outperforms monolingual pretraining. On the CommonVoice
benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared
to the best known results. On BABEL, our approach improves word error rate by
16% relative compared to a comparable system. Our approach enables a single
multilingual speech recognition model which is competitive to strong individual
models. Analysis shows that the latent discrete speech representations are
shared across languages with increased sharing for related languages. We hope
to catalyze research in low-resource speech understanding by releasing XLSR-53,
a large model pretrained in 53 languages.
