Towards spoken dialect identification of Irish
Liam Lonergan Mengjie Qian Neasa Ní Chiaráin Christer Gobl Ailbhe Ní Chasaide
The Irish language is rich in its diversity of dialects and accents. This
compounds the difficulty of creating a speech recognition system for the
low-resource language, as such a system must contend with a high degree of
variability with limited corpora. A recent study investigating dialect bias in
Irish ASR found that balanced training corpora gave rise to unequal dialect
performance, with performance for the Ulster dialect being consistently worse
than for the Connacht or Munster dialects. Motivated by this, the present
experiments investigate spoken dialect identification of Irish, with a view to
incorporating such a system into the speech recognition pipeline. Two acoustic
classification models are tested, XLS-R and ECAPA-TDNN, in conjunction with a
text-based classifier using a pretrained Irish-language BERT model. The
ECAPA-TDNN, particularly a model pretrained for language identification on the
VoxLingua107 dataset, performed best overall, with an accuracy of 73%. This was
further improved to 76% by fusing the model's outputs with the text-based
model. The Ulster dialect was most accurately identified, with an accuracy of
94%, however the model struggled to disambiguate between the Connacht and
Munster dialects, suggesting a more nuanced approach may be necessary to
robustly distinguish between the dialects of Irish.