Cited by 637Speech Recognition and SynthesisSpeech and Audio ProcessingMusic and Audio Processing

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Unsupervised Cross-Lingual Representation Learning for Speech Recognition

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Conneau, Alexis, Baevski, Alexei, Collobert, Ronan, Mohamed, Abdelrahman, Auli, Michael·2021·Interspeech 2021

DOI 10.21437/interspeech.2021-329

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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.

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Authors: Conneau, A., Baevski, A., Collobert, R., Mohamed, A., & Auli, M.
Affiliations: Meta (United Kingdom) (GB)
ORCID: Auli, Michael
Venue: Interspeech 2021
Publisher: ISCA
Published: 2021-08-30
Pages: 2426-2430
ISSN: 2308-457X (ISSN-L)
DOI: 10.21437/interspeech.2021-329
OpenAlex: W3037057938
Cited by: 637
References: 47
Citations by year: 2026: 60 · 2025: 136 · 2024: 115 · 2023: 155 · 2022: 103 · 2021: 61
FWCI: 50.12
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: Interspeech (USB) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Keywords: cross-lingual representation learning, multilingual speech recognition, XLS-R, wav2vec 2.0, contrastive learning, shared latent representation, Common Voice, Babel

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Unsupervised Cross-Lingual Representation Learning for Speech Recognition

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