PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a
  Diffusion Probabilistic Model
Yukiya Hono Kei Hashimoto Yoshihiko Nankaku Keiichi Tokuda
This paper presents a neural vocoder based on a denoising diffusion
probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary
conditioning signals. Recently, DDPM-based neural vocoders have gained
prominence as non-autoregressive models that can generate high-quality
waveforms. The neural vocoders based on DDPM have the advantage of training
with a simple time-domain loss. In practical applications, such as singing
voice synthesis, there is a demand for neural vocoders to generate
high-fidelity speech waveforms with flexible pitch control. However,
conventional DDPM-based neural vocoders struggle to generate speech waveforms
under such conditions. Our proposed model aims to accurately capture the
periodic structure of speech waveforms by incorporating explicit periodic
signals. Experimental results show that our model improves sound quality and
provides better pitch control than conventional DDPM-based neural vocoders.