Scaling A Simple Approach to Zero-Shot Speech Recognition
<|reference_start|>Scaling A Simple Approach to Zero-Shot Speech Recognition: 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.<|reference_end|>
@article{zhao2024scaling,
    title={Scaling A Simple Approach to Zero-Shot Speech Recognition},
    author={Jinming Zhao, Vineel Pratap, Michael Auli},
    journal={arXiv preprint arXiv:2407.17852},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17852},
    primaryClass={cs.CL}
}