  Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and
automatic speech recognition systems. Most g2p systems are monolingual: they
require language-specific data or handcrafting of rules. Such systems are
difficult to extend to low resource languages, for which data and handcrafted
rules are not available. As an alternative, we present a neural
sequence-to-sequence approach to g2p which is trained on
spelling--pronunciation pairs in hundreds of languages. The system shares a
single encoder and decoder across all languages, allowing it to utilize the
intrinsic similarities between different writing systems. We show an 11%
improvement in phoneme error rate over an approach based on adapting
high-resource monolingual g2p models to low-resource languages. Our model is
also much more compact relative to previous approaches.
