  There has been a growing interest in using end-to-end acoustic models for
singing voice synthesis (SVS). Typically, these models require an additional
vocoder to transform the generated acoustic features into the final waveform.
However, since the acoustic model and the vocoder are not jointly optimized, a
gap can exist between the two models, leading to suboptimal performance.
Although a similar problem has been addressed in the TTS systems by
joint-training or by replacing acoustic features with a latent representation,
adopting corresponding approaches to SVS is not an easy task. How to improve
the joint-training of SVS systems has not been well explored. In this paper, we
conduct a systematic investigation of how to better perform a joint-training of
an acoustic model and a vocoder for SVS. We carry out extensive experiments and
demonstrate that our joint-training strategy outperforms baselines, achieving
more stable performance across different datasets while also increasing the
interpretability of the entire framework.
