Disambiguation of Chinese Polyphones in an End-to-End Framework with Semantic Features Extracted by Pre-Trained BERT

Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this paper, we propose an end-to-end framework to predict the pronunciation of polyphonic character, which accepts sentence containing polyphonic character as input in the form of Chinese character sequence without the necessity of any preprocessing. The proposed method consists of a pre-trained bidirectional encoder representations from Transformers (BERT) model and a neural network (NN) based classifier. The pre-trained BERT model extracts semantic features from raw Chinese character sequence and the NN based classifier predicts the polyphonic character’s pronunciation according to BERT output. To explore the impact of contextual information on polyphone disambiguation, three different classifiers are investigated: a fully-connected network based classifier, a long short-term memory (LSTM) network based classifier and a Transformer block based classifier. Experimental results demonstrate the effectiveness of the proposed end-to-end framework for polyphone disambiguation and the semantic features extracted by BERT can greatly enhance the performance.