# N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement

arXiv: https://arxiv.org/abs/2106.15205 · HTML: https://mlsanity.com/paper/2106.15205/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Avocodo: Generative Adversarial Network for Artifact-free Vocoder](https://mlsanity.com/paper/2206.13404/) (2022)
- [HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models](https://mlsanity.com/paper/2306.06814/) (2023)
- [A Melody-Unsupervision Model for Singing Voice Synthesis](https://mlsanity.com/paper/2110.06546/) (2021)
- [WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses](https://mlsanity.com/paper/2203.10750/) (2022)
- [YingMusic-Singer: Zero-shot Singing Voice Synthesis and Editing with Annotation-free Melody Guidance](https://mlsanity.com/paper/2512.04779/) (2025)
- [MakeSinger: A Semi-Supervised Training Method for Data-Efficient Singing Voice Synthesis via Classifier-free Diffusion Guidance](https://mlsanity.com/paper/2406.05965/) (2024)
- [LAPS-Diff: A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning](https://mlsanity.com/paper/2507.04966/) (2025)

## Abstract (arXiv)

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.