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Text to speech (TTS) has made rapid progress in both academia and industry inrecent years. Some questions naturally arise that whether a TTS system canachieve human-level quality, how to define/judge that quality and how toachieve it. In this paper, we answer these questions by first defining thehuman-level quality based on the statistical significance of subjective measureand introducing appropriate guidelines to judge it, and then developing a TTSsystem called NaturalSpeech that achieves human-level quality on a benchmarkdataset. Specifically, we leverage a variational autoencoder (VAE) forend-to-end text to waveform generation, with several key modules to enhance thecapacity of the prior from text and reduce the complexity of the posterior fromspeech, 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 proposedNaturalSpeech achieves -0.01 CMOS (comparative mean opinion score) to humanrecordings at the sentence level, with Wilcoxon signed rank test at p-level p>> 0.05, which demonstrates no statistically significant difference from humanrecordings for the first time on this dataset.

NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality