  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.
