  Singing Voice Synthesis (SVS) has witnessed significant advancements with the
advent of deep learning techniques. However, a significant challenge in SVS is
the scarcity of labeled singing voice data, which limits the effectiveness of
supervised learning methods. In response to this challenge, this paper
introduces a novel approach to enhance the quality of SVS by leveraging
unlabeled data from pre-trained self-supervised learning models. Building upon
the existing VISinger2 framework, this study integrates additional spectral
feature information into the system to enhance its performance. The integration
aims to harness the rich acoustic features from the pre-trained models, thereby
enriching the synthesis and yielding a more natural and expressive singing
voice. Experimental results in various corpora demonstrate the efficacy of this
approach in improving the overall quality of synthesized singing voices in both
objective and subjective metrics.
