Massively Multilingual Neural Grapheme-to-Phoneme Conversion
Ben Peters, Jon Dehdari, Josef van Genabith · 2017
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.