A Low-Resource Pipeline for Text-to-Speech from Found Data With Application to Scottish Gaelic

In this work we present an end-to-end pipeline for building a speech corpus and text-to-speech synthesis system for a new language without reference to any expert-defined linguistic resources. We segment and align over 85 hours of Scottish Gaelic recordings found online and select 2- and 8-hour subsets with comprehensive coverage of speech sounds based on self-supervised discrete acoustic unit sequences. We then compare FastPitch models trained on these relatively small data sets using character, acoustic unit and phone inputs. According to native speaker listening test judgements, characters serve well for Gaelic given its regular orthography, even in these limited data scenarios. We release our corpus building recipe so that others may easily apply our work to new languages.