  In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a
quasi-periodic (QP) architecture to improve the pitch controllability of PWG.
PWG is a compact non-autoregressive (non-AR) speech generation model, whose
generative speed is much faster than real time. While utilizing PWG as a
vocoder to generate speech on the basis of acoustic features such as spectral
and prosodic features, PWG generates high-fidelity speech. However, when the
input acoustic features include unseen pitches, the pitch accuracy of
PWG-generated speech degrades because of the fixed and generic network of PWG
without prior knowledge of speech periodicity. The proposed QPPWG adopts a
pitch-dependent dilated convolution network (PDCNN) module, which introduces
the pitch information into PWG via the dynamically changed network
architecture, to improve the pitch controllability and speech modeling
capability of vanilla PWG. Both objective and subjective evaluation results
show the higher pitch accuracy and comparable speech quality of QPPWG-generated
speech when the QPPWG model size is only 70 % of that of vanilla PWG.
