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Novel two-stage model for grapheme-to-phoneme conversion using new grapheme generation rules

The quality of a grapheme-to-phoneme (G2P) conversion plays an important role in developing high quality speech synthesis systems. Because many problems regarding the G2P conversion have been reported, we propose a novel two-stage model-based approach, which is implemented using an existing Weighted Finite-State Transducer-based G2P conversion framework, to improve the performance of the G2P conversion model. The first stage model is built for automatic conversion of word to phonemes, while the second stage model utilizes the input graphemes and output phonemes obtained from the first-stage to determine the best final output phoneme sequence. Additionally, we design new grapheme generation rules, which enable extra detail for the vowel graphemes appearing within a word. When compared with previous approaches, the evaluation results show that our approach slightly improves the accuracy of the out-of-vocabulary dataset and consistently increases the accuracy of the in-vocabulary dataset.