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arXiv · 2307.07295

Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?

Liam Lonergan·Mengjie Qian·Neasa Ní Chiaráin·Christer Gobl·Ailbhe Ní Chasaide
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Abstract

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.

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Liam Lonergan, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, Ailbhe Ní Chasaide. 2023-07-14. Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?. https://arxiv.org/abs/2307.07295

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