Papers › g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a...

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

7 Apr 2020arXiv:2004.03136archive 2025-07-28

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 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 neural network model on it, and find that it outperforms other preexisting G2P systems. Finally, we package our project and share it on PyPi.

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Code

kakaobrain/g2pM officialmentioned in papermentioned on GitHub report

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Tasks

Polyphone disambiguationText to Speechtext-to-speech

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Datasets

Introduced by this paper, per the archive.

CPP

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Polyphone disambiguation CPP g2pM (BERT) Accuracy 97.85 #2 of 3 Archive leaderboard report
Polyphone disambiguation CPP g2pM (BiLSTM) Accuracy 97.31 #3 of 3 Archive leaderboard report

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Methods

LSTMSigmoid ActivationTanh Activation

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