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 self-supervised
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.

介绍了 PnG BERT，它是一种新的神经 TTS 编码器模型，使用输入的音素和字素表示以及它们之间的单词级对齐来增强原始的 BERT 模型。该模型可以进行自我监督的大型文本语料库的预训练，并通过 TTS 任务进行微调。实验结果表明，使用 PnG BERT 作为编码器进行预训练的神经 TTS 模型产生的语音韵律更自然，发音更准确，比仅使用音素输入而不预训练的基线模型表现更好。主观音频偏好评估表明，听者对使用 PnG BERT 合成的语音和专业演讲者的真实录音之间没有显着的统计差异。