This paper presents a high quality singing synthesizer that is able to model
a voice with limited available recordings. Based on the sequence-to-sequence
singing model, we design a multi-singer framework to leverage all the existing
singing data of different singers. To attenuate the issue of musical score
unbalance among singers, we incorporate an adversarial task of singer
classification to make encoder output less singer dependent. Furthermore, we
apply multiple random window discriminators (MRWDs) on the generated acoustic
features to make the network be a GAN. Both objective and subjective
evaluations indicate that the proposed synthesizer can generate higher quality
singing voice than baseline (4.12 vs 3.53 in MOS). Especially, the articulation
of high-pitched vowels is significantly enhanced.

本文提出了一种高质量的歌唱合成器，它能够在有限的可用录音基础上模拟出一种声音，采用序列到序列的歌唱模型，并设计了一个多歌手框架来利用不同歌手的现有歌唱数据，以减轻歌唱评分不平衡的问题。此外，为了使编码器输出与歌手无关，我们加入了一个对抗性任务来保证模型的平衡性及多随机窗口鉴别器（MRWDs）以使网络成为 GAN。客观和主观的评估表明，所提出的合成器比基准测试能够产生更高质量的歌唱声音（MOS 值分别为 4.12 和 3.53）。特别是高音元音的表达得到了显著改善。