  Recently, deep learning-based generative models have been introduced to
generate singing voices. One approach is to predict the parametric vocoder
features consisting of explicit speech parameters. This approach has the
advantage that the meaning of each feature is explicitly distinguished. Another
approach is to predict mel-spectrograms for a neural vocoder. However,
parametric vocoders have limitations of voice quality and the mel-spectrogram
features are difficult to model because the timbre and pitch information are
entangled. In this study, we propose a singing voice synthesis model with
multi-task learning to use both approaches -- acoustic features for a
parametric vocoder and mel-spectrograms for a neural vocoder. By using the
parametric vocoder features as auxiliary features, the proposed model can
efficiently disentangle and control the timbre and pitch components of the
mel-spectrogram. Moreover, a generative adversarial network framework is
applied to improve the quality of singing voices in a multi-singer model.
Experimental results demonstrate that our proposed model can generate more
natural singing voices than the single-task models, while performing better
than the conventional parametric vocoder-based model.
