Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio

Bootstrapping speech recognition on limited data resources has been an area of active research for long. The recent transition to all-neural models and end-to-end (E2E) training brought along particular challenges as these models are known to be data hungry, but also came with opportunities around language-agnostic representations derived from multilingual data as well as shared word-piece output representations across languages that share script and roots.Here, we investigate the effectiveness of different strategies to bootstrap an RNN Transducer (RNN-T) based automatic speech recognition (ASR) system in the low resource regime,while exploiting the abundant resources available in other languages as well as the synthetic audio from a text-to-speech(TTS) engine. Experiments show that the combination of a multilingual RNN-T word-piece model, post-ASR text-to-text mapping, and synthetic audio can effectively bootstrap an ASR system for a new language in a scalable fashion with little target language data.