A data-driven grapheme-to-phoneme conversion method using dynamic contextual converting rules for Korean TTS systems

Abstract

This paper presents a data-driven Korean grapheme-to-phoneme conversion method including alignment, rule extraction, and rule pruning procedures. Novel rule extraction and pruning techniques are introduced to effectively handle the exceptional pronunciation of speech databases. The performances with the full rules and the reduced rules are 99.22% and 99.11% phoneme level accuracy, respectively. Compared with state-of-the-art previous works, the experimental results show that our method is very promising.