They aren't hiding that, though? Literally the first graphic on the page shows their claim of a "word error rate" on test data has an error rate of around 14% in the best case compared to the state of the art at 15% (for en-US content).

It's only 1% better than the current state of the art. But it's still noteworthy. From the end of the abstract:

> We demonstrate that utilizing a large unlabeled multilingual dataset to pre-train the encoder of our model and fine-tuning on a smaller set of labeled data enables us to recognize these under-represented languages. Moreover, our model training process is effective for adapting to new languages and data.

It's amazing to me that the chaotic process of "machine learning" can end up with an internal state for languages that is readily adapted to entirely new languages.

For now, they've got this handling audio transcription, but with some hints that this approach could work well for translation. Perhaps we'll be able to use these improved models to decipher Linear A[0] or other un-deciphered languages. It sounds like "magic", but it's the kind that could maybe exist.

[0] https://en.wikipedia.org/wiki/Linear_A