Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages. (arXiv:2208.12666v1 [cs.CL])

End-to-end (E2E) models have become the default choice for state-of-the-art
speech recognition systems. Such models are trained on large amounts of
labelled data, which are often not available for low-resource languages.
Techniques such as self-supervised learning and transfer learning hold promise,
but have not yet been effective in training accurate models. On the other hand,
collecting labelled datasets on a diverse set of domains and speakers is very
expensive. In this work, we demonstrate an inexpensive and effective
alternative to …

arxiv asr audio data mining public public data systems text