ASR systems are generally built for the spoken 'standard', and their performance declines for non-standard dialects/varieties. This is a problem for a language like Irish, where there is no single spoken standard, but rather three major dialects: Ulster (Ul), Connacht (Co) and Munster (Mu). As a diagnostic to quantify the effect of the speaker's dialect on recognition performance, 12 ASR systems were trained, firstly using baseline dialect-balanced training corpora, and then using modified versions of the baseline corpora, where dialect-specific materials were either subtracted or added. Results indicate that dialect-balanced corpora do not yield a similar performance across the dialects: the Ul dialect consistently underperforms, whereas Mu yields lowest WERs. There is a close relationship between Co and Mu dialects, but one that is not symmetrical. These results will guide future corpus collection and system building strategies to optimise for cross-dialect performance equity.
@article{arxiv.2307.07295,
title = {Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?},
author = {Liam Lonergan and Mengjie Qian and Neasa Ní Chiaráin and Christer Gobl and Ailbhe Ní Chasaide},
journal= {arXiv preprint arXiv:2307.07295},
year = {2023}
}