Universal Grapheme-to-Phoneme Prediction Over Latin Alphabets - Kim, Young-Bum (Empirical Methods in Natural Language Processing (EMNLP), 2012)
We consider the problem of inducing grapheme-to-phoneme mappings for unknown languages written in a Latin alphabet. First, we collect a data-set of 107 languages with known grapheme-phoneme relationships, along with a short text in each language. We then cast our task in the framework of supervised learning, where each known language serves as a training example, and predictions are made on unknown languages. We induce an undirected graphical model that learns phonotactic regularities, thus relating textual patterns to plausible phonemic interpretations across the entire range of languages. Our model correctly predicts grapheme-phoneme pairs with over 88% F1-measure.