g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset

Kyubyong Park, Seanie Lee


Conversion of Chinese graphemes to phonemes (G2P) is an essential component in Mandarin Chinese Text-To-Speech (TTS) systems. One of the biggest challenges in Chinese G2P conversion is how to disambiguate the pronunciation of polyphones — characters having multiple pronunciations. Although many academic efforts have been made to address it, there has been no open dataset that can serve as a standard benchmark for a fair comparison to date. In addition, most of the reported systems are hard to employ for researchers or practitioners who want to convert Chinese text into pinyin at their convenience. Motivated by these, in this work, we introduce a new benchmark dataset that consists of 99,000+ sentences for Chinese polyphone disambiguation. We train a simple Bi-LSTM model on it and find that it outperforms other pre-existing G2P systems and slightly underperforms pre-trained Chinese BERT. Finally, we package our project and share it on PyPi.


 DOI: 10.21437/Interspeech.2020-1094

Cite as: Park, K., Lee, S. (2020) g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset. Proc. Interspeech 2020, 1723-1727, DOI: 10.21437/Interspeech.2020-1094.


@inproceedings{Park2020,
  author={Kyubyong Park and Seanie Lee},
  title={{g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={1723--1727},
  doi={10.21437/Interspeech.2020-1094},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1094}
}