  Grapheme-to-phoneme (G2P) conversion is an indispensable part of the Chinese
Mandarin text-to-speech (TTS) system, and the core of G2P conversion is to
solve the problem of polyphone disambiguation, which is to pick up the correct
pronunciation for several candidates for a Chinese polyphonic character. In
this paper, we propose a Chinese polyphone BERT model to predict the
pronunciations of Chinese polyphonic characters. Firstly, we create 741 new
Chinese monophonic characters from 354 source Chinese polyphonic characters by
pronunciation. Then we get a Chinese polyphone BERT by extending a pre-trained
Chinese BERT with 741 new Chinese monophonic characters and adding a
corresponding embedding layer for new tokens, which is initialized by the
embeddings of source Chinese polyphonic characters. In this way, we can turn
the polyphone disambiguation task into a pre-training task of the Chinese
polyphone BERT. Experimental results demonstrate the effectiveness of the
proposed model, and the polyphone BERT model obtain 2% (from 92.1% to 94.1%)
improvement of average accuracy compared with the BERT-based classifier model,
which is the prior state-of-the-art in polyphone disambiguation.
