N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement
Gyeong-Hoon Lee, Taewoo Kim, Hanbin Bae, Minji Lee, Young-Ik Kim, Hoon Young Cho · 2021
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 nonautoregressive 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 networkbased 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.