Recently, end-to-end Korean singing voice systems have been designed to
generate realistic singing voices. However, these systems still suffer from a
lack of robustness in terms of pronunciation accuracy. In this paper, we
propose N-Singer, a non-autoregressive Korean singing voice system, to
synthesize accurate and pronounced Korean singing voices in parallel. N-Singer
consists of a Transformer-based mel-generator, a convolutional network-based
postnet, and voicing-aware discriminators. It can contribute in the following
ways. First, for accurate pronunciation, N-Singer separately models linguistic
and pitch information without other acoustic features. Second, to achieve
improved mel-spectrograms, N-Singer uses a combination of Transformer-based
modules and convolutional network-based modules. Third, in adversarial
training, voicing-aware conditional discriminators are used to capture the
harmonic features of voiced segments and noise components of unvoiced segments.
The experimental results prove that N-Singer can synthesize a natural singing
voice in parallel with a more accurate pronunciation than the baseline model.

本文提出一种名为 N-Singer 的韩语歌声合成系统，通过用 Transformer-based mel-generator、卷积网络 - based postnet 和声音感知判别器来分别建模语言和音高信息以实现更准确的韩语发音和改善谱图。