  This work focuses on modelling a speaker's accent that does not have a
dedicated text-to-speech (TTS) frontend, including a grapheme-to-phoneme (G2P)
module. Prior work on modelling accents assumes a phonetic transcription is
available for the target accent, which might not be the case for low-resource,
regional accents. In our work, we propose an approach whereby we first augment
the target accent data to sound like the donor voice via voice conversion, then
train a multi-speaker multi-accent TTS model on the combination of recordings
and synthetic data, to generate the donor's voice speaking in the target
accent. Throughout the procedure, we use a TTS frontend developed for the same
language but a different accent. We show qualitative and quantitative analysis
where the proposed strategy achieves state-of-the-art results compared to other
generative models. Our work demonstrates that low resource accents can be
modelled with relatively little data and without developing an accent-specific
TTS frontend. Audio samples of our model converting to multiple accents are
available on our web page.
