Pretraining by Backtranslation for End-to-end ASR in Low-Resource\n Settings
Journal: arXiv (Cornell University)
Year: 2018

We explore training attention-based encoder-decoder ASR in low-resource\nsettings. These models perform poorly when trained on small amounts of\ntranscribed speech, in part because they depend on having sufficient\ntarget-side text to train the attention and decoder networks. In this paper we\naddress this shortcoming by pretraining our network parameters using only\ntext-based data and transcribed speech from other languages. We analyze the\nrelative contributions of both sources of data. Acros