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COMPUTER SCIENCE > COMPUTATION AND LANGUAGE

arXiv:2006.13979v2 (cs)
[Submitted on 24 Jun 2020 (v1), last revised 15 Dec 2020 (this version, v2)]


TITLE:UNSUPERVISED CROSS-LINGUAL REPRESENTATION LEARNING FOR SPEECH RECOGNITION

Authors:Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed,
Michael Auli
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> Abstract: 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.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound
(cs.SD); Audio and Speech Processing (eess.AS) Cite as: arXiv:2006.13979 [cs.CL]
  (or arXiv:2006.13979v2 [cs.CL] for this version)  
https://doi.org/10.48550/arXiv.2006.13979
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arXiv-issued DOI via DataCite


SUBMISSION HISTORY

From: Alexis Conneau [view email]
[v1] Wed, 24 Jun 2020 18:25:05 UTC (282 KB)
[v2] Tue, 15 Dec 2020 23:19:19 UTC (660 KB)

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Alexis Conneau
Alexei Baevski
Ronan Collobert
Abdelrahman Mohamed
Michael Auli
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