PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
Jia Ye, Heiga Zen, Jonathan Shen, Yu Zhang, Yonghui Wu
Abstract
This paper introduces PnG BERT, a new encoder model for neural TTS.This model is augmented from the original BERT model, by taking both phoneme and grapheme representations of text as input, as well as the word-level alignment between them.It can be pre-trained on a large text corpus in a selfsupervised manner, and fine-tuned in a TTS task.Experimental results show that a neural TTS model using a pre-trained PnG BERT as its encoder yields more natural prosody and more accurate pronunciation than a baseline model using only phoneme input with no pre-training.Subjective side-by-side preference evaluations show that raters have no statistically significant preference between the speech synthesized using a PnG BERT and ground truth recordings from professional speakers.