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

arXiv: https://arxiv.org/abs/2004.03136 · HTML: https://mlsanity.com/paper/2004.03136/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://mlsanity.com/paper/2112.02721/) (2021)
- [ByT5 model for massively multilingual grapheme-to-phoneme conversion](https://mlsanity.com/paper/2204.03067/) (2022)
- [g2pW: A Conditional Weighted Softmax BERT for Polyphone Disambiguation in Mandarin](https://mlsanity.com/paper/2203.10430/) (2022)
- [Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation](https://mlsanity.com/paper/2211.09495/) (2022)
- [Polyphone Disambiguation in Mandarin Chinese with Semi-Supervised Learning](https://mlsanity.com/paper/2102.00621/) (2021)
- [MM-KWS: Multi-modal Prompts for Multilingual User-defined Keyword Spotting](https://mlsanity.com/paper/2406.07310/) (2024)
- [External Knowledge Augmented Polyphone Disambiguation Using Large Language Model](https://mlsanity.com/paper/2312.11920/) (2023)
- [The MSXF TTS System for ICASSP 2022 ADD Challenge](https://mlsanity.com/paper/2201.11400/) (2022)
- [Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech](https://mlsanity.com/paper/2206.02147/) (2022)
- [Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation](https://mlsanity.com/paper/2210.10200/) (2022)
- [GSA-TTS : Toward Zero-Shot Speech Synthesis based on Gradual Style Adaptor](https://mlsanity.com/paper/2505.19384/) (2025)
- [Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation](https://mlsanity.com/paper/2605.02496/) (2026)
- [Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese](https://mlsanity.com/paper/2606.07300/) (2026)
- [CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation](https://mlsanity.com/paper/2606.24714/) (2026)

## Abstract (arXiv)

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.