  Despite rapid progress in increasing the language coverage of automatic
speech recognition, the field is still far from covering all languages with a
known writing script. Recent work showed promising results with a zero-shot
approach requiring only a small amount of text data, however, accuracy heavily
depends on the quality of the used phonemizer which is often weak for unseen
languages. In this paper, we present MMS Zero-shot a conceptually simpler
approach based on romanization and an acoustic model trained on data in 1,078
different languages or three orders of magnitude more than prior art. MMS
Zero-shot reduces the average character error rate by a relative 46% over 100
unseen languages compared to the best previous work. Moreover, the error rate
of our approach is only 2.5x higher compared to in-domain supervised baselines,
while our approach uses no labeled data for the evaluation languages at all.
