G2PL: Lexicon Enhanced Chinese Polyphone Disambiguation using BERT Adapter with a New Dataset

Polyphone disambiguation is the core of grapheme-to phoneme(G2P) module for the Chinese speech synthesis system. However, there is a lack of datasets and only one public for polyphone disambiguation. Moreover, due to the double long-tail distribution of polyphones, the ratio of pronunciation data for most polyphones is extremely unbalanced after sampling. To solve these problems, we propose a new dataset with 57,000 sentences from various domains by a new strategy for sampling. In addition, we propose the G2PL, which integrates word features into the bottom of BERT to assist in predicting the correct pronunciation of polyphone. In the experiment, we train the G2PL model to outperform other methods on our and public datasets. Our dataset, codes and user-friendly package are freely available.