Conversion of Chinese Grapheme-to-Phoneme (G2P) plays an important role in
Mandarin Chinese Text-To-Speech (TTS) systems, where one of the biggest
challenges is the task of polyphone disambiguation. Most of the previous
polyphone disambiguation models are trained on manually annotated datasets, and
publicly available datasets for polyphone disambiguation are scarce. In this
paper we propose a simple back-translation-style data augmentation method for
mandarin Chinese polyphone disambiguation, utilizing a large amount of
unlabeled text data. Inspired by the back-translation technique proposed in the
field of machine translation, we build a Grapheme-to-Phoneme (G2P) model to
predict the pronunciation of polyphonic character, and a Phoneme-to-Grapheme
(P2G) model to predict pronunciation into text. Meanwhile, a window-based
matching strategy and a multi-model scoring strategy are proposed to judge the
correctness of the pseudo-label. We design a data balance strategy to improve
the accuracy of some typical polyphonic characters in the training set with
imbalanced distribution or data scarcity. The experimental result shows the
effectiveness of the proposed back-translation-style data augmentation method.

本文提出了一种基于回译的数据增强方法，采用大量未标注的文本数据来解决中文多音字消歧问题，并设计了一种数据平衡策略来提高训练集中某些典型的多音字字符的识别准确性。