Unsupervised Cross-lingual Representation Learning for Speech\n Recognition
Journal: arXiv (Cornell University)
Year: 2020

This paper presents XLSR which learns cross-lingual speech representations by\npretraining a single model from the raw waveform of speech in multiple\nlanguages. We build on wav2vec 2.0 which is trained by solving a contrastive\ntask over masked latent speech representations and jointly learns a\nquantization of the latents shared across languages. The resulting model is\nfine-tuned on labeled data and experiments show that cross-lingual pretraining\nsignificantly outperforms monolingual pretrai