Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages

Understand

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.