# OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network

arXiv: https://arxiv.org/abs/1912.13321 · HTML: https://mlsanity.com/paper/1912.13321/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Learning to pronounce as measuring cross-lingual joint orthography-phonology complexity](https://mlsanity.com/paper/2202.00794/) (2022)
- [Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation](https://mlsanity.com/paper/2210.02509/) (2022)
- [A primer on getting neologisms from foreign languages to under-resourced languages](https://mlsanity.com/paper/2304.10495/) (2023)
- [Evaluation of Phonetic Encoding Algorithms on Transcription Datasets](https://mlsanity.com/paper/2609.04391/) (2026)

## Abstract (arXiv)

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.