  To transcribe spoken language to written medium, most alphabets enable an
unambiguous sound-to-letter rule. However, some writing systems have distanced
themselves from this simple concept and little work exists in Natural Language
Processing (NLP) on measuring such distance. In this study, we use an
Artificial Neural Network (ANN) model to evaluate the transparency between
written words and their pronunciation, hence its name Orthographic Transparency
Estimation with an ANN (OTEANN). Based on datasets derived from Wikimedia
dictionaries, we trained and tested this model to score the percentage of
correct predictions in phoneme-to-grapheme and grapheme-to-phoneme translation
tasks. The scores obtained on 17 orthographies were in line with the
estimations of other studies. Interestingly, the model also provided insight
into typical mistakes made by learners who only consider the phonemic rule in
reading and writing.
