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

• URL: http://arxiv.org/abs/2011.12696v2
• Date: Fri, 18 Jun 2021 12:12:44 GMT
• Title: Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio
• Authors: Manuel Giollo, Deniz Gunceler, Yulan Liu, Daniel Willett
• Abstract summary: bootstrapping speech recognition on limited data resources has been an area of active research for long. We investigate here the effectiveness of different strategies to bootstrap an RNN-Transducer based automatic speech recognition (ASR) system in the low resource regime. Our experiments demonstrate that transfer learning from a multilingual model, using a post-ASR text-to-text mapping and synthetic audio deliver additive improvements.
• Score: 8.510792628268824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We investigate here 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. Our experiments demonstrate that transfer learning from a multilingual model, using a post-ASR text-to-text mapping and synthetic audio deliver additive improvements, allowing us to bootstrap a model for a new language with a fraction of the data that would otherwise be needed. The best system achieved a 46% relative word error rate (WER) reduction compared to the monolingual baseline, among which 25% relative WER improvement is attributed to the post-ASR text-to-text mappings and the TTS synthetic data.

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