Text to speech (TTS) has made rapid progress in both academia and industry in
recent years. Some questions naturally arise that whether a TTS system can
achieve human-level quality, how to define/judge that quality and how to
achieve it. In this paper, we answer these questions by first defining the
human-level quality based on the statistical significance of subjective measure
and introducing appropriate guidelines to judge it, and then developing a TTS
system called NaturalSpeech that achieves human-level quality on a benchmark
dataset. Specifically, we leverage a variational autoencoder (VAE) for
end-to-end text to waveform generation, with several key modules to enhance the
capacity of the prior from text and reduce the complexity of the posterior from
speech, including phoneme pre-training, differentiable duration modeling,
bidirectional prior/posterior modeling, and a memory mechanism in VAE.
Experiment evaluations on popular LJSpeech dataset show that our proposed
NaturalSpeech achieves -0.01 CMOS (comparative mean opinion score) to human
recordings at the sentence level, with Wilcoxon signed rank test at p-level p
>> 0.05, which demonstrates no statistically significant difference from human
recordings for the first time on this dataset.

本文提出了一种基于变分自动编码器的 TTS 系统 NaturalSpeech，通过多个关键模块提高文本先验的容量和语音后验的复杂度，同时在 LJSpeech 数据集上实验证明该系统在句子级别上达到了人类录音的 - 0.01 CMOS（比较平均意见分数），与人类录音不存在显著性差异。