Bootstrap an end-to-end ASR system by multilingual training, transfer\n learning, text-to-text mapping and synthetic audio
Journal: arXiv (Cornell University)
Year: 2020

Bootstrapping speech recognition on limited data resources has been an area\nof active research for long. The recent transition to all-neural models and\nend-to-end (E2E) training brought along particular challenges as these models\nare known to be data hungry, but also came with opportunities around\nlanguage-agnostic representations derived from multilingual data as well as\nshared word-piece output representations across languages that share script and\nroots. We investigate here the effectiv