The single-speaker singing voice synthesis (SVS) usually underperforms at
pitch values that are out of the singer's vocal range or associated with
limited training samples. Based on our previous work, this work proposes a
melody-unsupervised multi-speaker pre-training method conducted on a
multi-singer dataset to enhance the vocal range of the single-speaker, while
not degrading the timbre similarity. This pre-training method can be deployed
to a large-scale multi-singer dataset, which only contains audio-and-lyrics
pairs without phonemic timing information and pitch annotation. Specifically,
in the pre-training step, we design a phoneme predictor to produce the
frame-level phoneme probability vectors as the phonemic timing information and
a speaker encoder to model the timbre variations of different singers, and
directly estimate the frame-level f0 values from the audio to provide the pitch
information. These pre-trained model parameters are delivered into the
fine-tuning step as prior knowledge to enhance the single speaker's vocal
range. Moreover, this work also contributes to improving the sound quality and
rhythm naturalness of the synthesized singing voices. It is the first to
introduce a differentiable duration regulator to improve the rhythm naturalness
of the synthesized voice, and a bi-directional flow model to improve the sound
quality. Experimental results verify that the proposed SVS system outperforms
the baseline on both sound quality and naturalness.

基于先前的研究，在一个多歌手的数据集上进行的旋律无监督多说话人预训练方法，提高了单个说话人的声域，同时不降低音色相似性。这种预训练方法可以应用于只包含音频和歌词对的大规模多歌手数据集，并改善了合成歌声的音质和节奏自然度。