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.

XLSR 是一种学习跨语言语音表示的模型，通过对多种语言的语音原始波形进行预训练来构建模型，上述模型建立在 wav2vec 2.0 的基础之上，可以使用单一模型完成多语言语音识别任务，并且具有更好的性能。