Abstract. The precise conversion of arbitrary text into its corresponding
phoneme sequence (grapheme-to-phoneme or G2P conversion) is implemented
in speech synthesis and recognition, pronunciation learning software, spoken
term detection and spoken document retrieval systems. Because the quality of
this module plays an important role in the performance of such systems and
many problems regarding 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 words 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 designed
new grapheme generation rules, which enable extra detail for the vowel and
consonant graphemes appearing within a word. When compared with previous
approaches, the evaluation results indicate that our approach using rules focusing
on the vowel graphemes slightly improved the accuracy of the out-of-vocabulary
dataset and consistently increased the accuracy of the in-vocabulary dataset