  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.
