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.

本文利用机器学习模型探索什么因素会使一种语言的发音难度加大，并通过跨越 22 种不同语言的字符级变换器模型训练和验证，发现语言的发音难度主要由其字音对音素的映射的复杂性而非其文字本身的复杂性决定，并提出未来的研究应该考虑数据稀少性，以设计更公正的跨语言比较任务。