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.
Cite
Text
Radford et al. "Robust Speech Recognition via Large-Scale Weak Supervision." International Conference on Machine Learning, 2023.Markdown
[Radford et al. "Robust Speech Recognition via Large-Scale Weak Supervision." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/radford2023icml-robust/)BibTeX
@inproceedings{radford2023icml-robust,
title = {{Robust Speech Recognition via Large-Scale Weak Supervision}},
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and Mcleavey, Christine and Sutskever, Ilya},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {28492-28518},
volume = {202},
url = {https://mlanthology.org/icml/2023/radford2023icml-robust/}
}