The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Unsupervised Cross-Lingual Representation Learning for Speech Recognition
Open accessAbstract
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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“Depending on the number of languages used for training, XLSR supports 53 languages (XLSR [124]) and 128 languages (XLS-R [125]).”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [124]
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
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Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637
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Research works printed but not linked: 2
- Mark JF Gales, Kate M Knill, and Anton Ragni. Low-resource speech recognition and keyword- spotting. In Proc. of SPECOM, pp. 3-19. Springer, 2017.
- Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. arXiv, 2019.
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XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu, Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y., Pino, J., et al. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-143 · Cited by 586
Self-citationFLAVA: A Foundational Language And Vision Alignment Model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., & Kiela, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01519 · Cited by 678
A comprehensive survey on applications of transformers for deep learning tasks
Islam, S., Elmekki, H., Elsebai, A., Bentahar, J., Drawel, N., Rjoub, G., & Witold Pedrycz, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122666 · Cited by 590
Self-supervised speech representation learning: A review
Mohamed, A., Lee, H.-Y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maaløe, L., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3207050 · Cited by 372
“In [282], a wav2vec 2.0 model was trained on data from multiple different languages and different corpora (Babel, Common Voice, and multilingual LS) jointly, followed by fine-tuning for each individual language.”
MethodInfluentialSelf-citationWang, C., Rivière, M., Lee, A., Wu, A., Talnikar, C., Haziza, D., Williamson, M., Pino, J., & Emmanuel Dupoux, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.80 · Cited by 383
Metadata
- 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)
- 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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