Developing Pronunciation Models in New Languages Faster by Exploiting Common Grapheme-to-Phoneme Correspondences Across Languages

We discuss two methods that let us easily create grapheme-to-phoneme (G2P) conversion systems for languages without any human-curated pronunciation lexicons, as long as we know the phoneme inventory of the target language and as long as we have some pronunciation lexicons for other languages written in the same script. We use these resources to infer what grapheme-to-phoneme correspondences we would expect, and predict pronunciations for words in the target language with minimal or no language-specific human work. Our first approach uses finite-state transducers, while our second approach uses a sequence-to-sequence neural network. Our G2P models reach high degrees of accuracy, and can be used for various applications, e.g. in developing an Automatic Speech Recognition system. Our methods greatly simplify a task that has historically required extensive manual labor.