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XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2111.09296

Summary

Self-supervised speech models had been scaled mainly for English or in supervised setups. Cross-lingual ones stayed small: XLSR-53 had about 300M parameters and roughly 50K hours of audio. XLS-R tests how far this approach can go. The authors pretrain wav2vec 2.0 Transformers with 0.3B, 1B and 2B parameters on 436K hours of unlabeled, publicly available speech covering 128 languages, about ten times more public data than earlier work. Training uses the standard wav2vec 2.0 recipe: masked spans are predicted contrastively against quantized targets, and languages are sampled with an upsampling factor so low-resource languages get more weight. The resulting models are fine-tuned for speech translation, speech recognition and language identification. They beat prior results on CoVoST-2, BABEL, MLS, CommonVoice, VoxPopuli and VoxLingua107. With enough capacity, the multilingual model even matches English-only pretraining when translating from English. The work matters because the open checkpoints, released through fairseq and Hugging Face, make strong speech technology much more reachable for low-resource languages, Ukrainian among them.

Key points

  • Pretraining data (436K hours, 128 languages):
    • VoxPopuli (VP-400K): 372K hours of European Parliament speech in 23 languages
    • MLS: about 50K hours in 8 languages
    • CommonVoice v6.1: over 7K hours in 60 languages
    • VoxLingua107: 6.6K hours of YouTube audio in 107 languages
    • BABEL: about 1K hours of telephone speech in 17 languages
  • Language balance: about 24 languages have more than 1K hours each, 17 fall between 100 and 1K hours, and 88 have under 100 hours.
  • CoVoST-2 X→English: Across 21 directions, XLS-R surpasses the previous best results by 7.4 BLEU on average. The 2B model averages 22.1 BLEU, against 14.7 for the best earlier system.
  • Speech recognition: relative error rates drop by 14–34% on average on BABEL, MLS, CommonVoice and VoxPopuli. XLS-R also sets a new best result for language identification on VoxLingua107.
  • English→X translation: the 2B XLS-R averages 27.8 BLEU. The English-only wav2vec 2.0 model (0.72B, LV-60K) reaches 26.6, so cross-lingual pretraining can rival monolingual pretraining at sufficient model size.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

244 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 239 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2111.09296 · arXiv 2111.09296

@misc{babu2021xls,
  title = {{XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale}},
  author = {Babu, Arun and Wang, Changhan and Tjandra, Andros and Lakhotia, Kushal and Xu, Qiantong and Goyal, Naman and Singh, Kritika and Platen, Patrick von and Saraf, Yatharth and Pino, Juan and Baevski, Alexei and Conneau, Alexis and Auli, Michael},
  year = {2021},
  doi = {10.48550/arxiv.2111.09296},
  eprint = {2111.09296},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2111.09296}
}

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