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Robust speech recognition via large-scale weak supervision

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Type de ressource
Conference Paper
Auteurs/contributeurs
Title
Robust speech recognition via large-scale weak supervision
Abstract
We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results without the need for any dataset specific fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.
Date
2023
Proceedings Title
Proceedings of the 40th International Conference on Machine Learning
Publisher
JMLR.org
Series
ICML'23
Extra
Place: Honolulu, Hawaii, USA
Référence
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large-scale weak supervision. Proceedings of the 40th International Conference on Machine Learning.
Tâche
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