  Machine learning models allow us to compare languages by showing how hard a
task in each language might be to learn and perform well on. Following this
line of investigation, we explore what makes a language "hard to pronounce" by
modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a
character-level transformer model on this task across 22 languages and
measuring the model's proficiency against its grapheme and phoneme inventories,
we show that certain characteristics emerge that separate easier and harder
languages with respect to learning to pronounce. Namely the complexity of a
language's pronunciation from its orthography is due to the expressive or
simplicity of its grapheme-to-phoneme mapping. Further discussion illustrates
how future studies should consider relative data sparsity per language to
design fairer cross-lingual comparison tasks.
