This paper presents XiaoiceSing, a high-quality singing voice synthesis
system which employs an integrated network for spectrum, F0 and duration
modeling. We follow the main architecture of FastSpeech while proposing some
singing-specific design: 1) Besides phoneme ID and position encoding, features
from musical score (e.g.note pitch and length) are also added. 2) To attenuate
off-key issues, we add a residual connection in F0 prediction. 3) In addition
to the duration loss of each phoneme, the duration of all the phonemes in a
musical note is accumulated to calculate the syllable duration loss for rhythm
enhancement. Experiment results show that XiaoiceSing outperforms the baseline
system of convolutional neural networks by 1.44 MOS on sound quality, 1.18 on
pronunciation accuracy and 1.38 on naturalness respectively. In two A/B tests,
the proposed F0 and duration modeling methods achieve 97.3% and 84.3%
preference rate over baseline respectively, which demonstrates the overwhelming
advantages of XiaoiceSing.

该论文介绍了 XiaoiceSing，这是一个高质量的歌唱合成系统，它采用集成网络进行频谱、 F0 和持续时间建模。该系统在保留 FastSpeech 的主要框架的同时提出了一些特定于歌唱的设计，通过多种实验验证该系统表现优异。