g2pM: A Neural Grapheme-to-Phoneme Conversion Package for MandarinChinese Based on a New Open Benchmark Dataset
By Kyubyong Park, Seanie Lee. 2020
39 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Tie-Yan Liu
- Tao Qin
- Xu Tan
- Mohit Bansal
- Sebastian Gehrmann
- Noah A. Smith
- Frank K. Soong
- Graham Neubig
Citations in the corpus
- A Survey on Neural Speech Synthesis (2021, methodology): “Thus, G2P conversion in this kind of languages is mainly responsible for polyphone disambiguation, which decides the appropriate pronunciation based on the current word context [441, 392, 224, 295, 321, 29, 257].”
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021, background): “The Character-to-Pinyin converter at the core of this transformation is a neural model by Park and Lee (2020).”
- Unified Mandarin TTS Front-end Based on Distilled BERT Model (2020, methodology): “BLSTM: Following a recent method of neural network based G2P[22], the network consists one BLSTM layer with character embeddings and POS tag embeddings as input, followed with two fully connected layers and a softmax layer to predict pronunciation.”