Robust Speech Recognition via Large-Scale Weak Supervision
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Cited by 161 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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 but in a zero-shot transfer setting without the need for any 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.
Key facts. Cited by 3,972 papers in the map (data through 7 Oct 2026). Builds on 57 of its 100 references that are in the map. First posted on arXiv 6 Dec 2022.
Builds on
57 of 100 references.
References · 57
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50 most cited of 57 All 57 (JSON)
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