  In this paper, we propose a quasi-periodic parallel WaveGAN (QPPWG) waveform
generative model, which applies a quasi-periodic (QP) structure to a parallel
WaveGAN (PWG) model using pitch-dependent dilated convolution networks
(PDCNNs). PWG is a small-footprint GAN-based raw waveform generative model,
whose generation time is much faster than real time because of its compact
model and non-autoregressive (non-AR) and non-causal mechanisms. Although PWG
achieves high-fidelity speech generation, the generic and simple network
architecture lacks pitch controllability for an unseen auxiliary fundamental
frequency ($F_{0}$) feature such as a scaled $F_{0}$. To improve the pitch
controllability and speech modeling capability, we apply a QP structure with
PDCNNs to PWG, which introduces pitch information to the network by dynamically
changing the network architecture corresponding to the auxiliary $F_{0}$
feature. Both objective and subjective experimental results show that QPPWG
outperforms PWG when the auxiliary $F_{0}$ feature is scaled. Moreover,
analyses of the intermediate outputs of QPPWG also show better tractability and
interpretability of QPPWG, which respectively models spectral and
excitation-like signals using the cascaded fixed and adaptive blocks of the QP
structure.
