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.

研究了影响语音自动识别（ASR）系统跨音系瓶颈的爱尔兰三个主要方言的对比分析，结果显示平衡方言的语料库无法在方言之间产生相似的表现，这些结果将指导未来语料收集和系统构建策略以优化跨方言表现公正性。