A polyphone BERT for Polyphone Disambiguation in Mandarin Chinese
Song Zhang, Ken Zheng, Xiaoxu Zhu, Baoxiang Li · Interspeech 2022 · 2022
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.