  We propose PeriodNet, a non-autoregressive (non-AR) waveform generation model
with a new model structure for modeling periodic and aperiodic components in
speech waveforms. The non-AR waveform generation models can generate speech
waveforms parallelly and can be used as a speech vocoder by conditioning an
acoustic feature. Since a speech waveform contains periodic and aperiodic
components, both components should be appropriately modeled to generate a
high-quality speech waveform. However, it is difficult to decompose the
components from a natural speech waveform in advance. To address this issue, we
propose a parallel model and a series model structure separating periodic and
aperiodic components. The features of our proposed models are that explicit
periodic and aperiodic signals are taken as input, and external
periodic/aperiodic decomposition is not needed in training. Experiments using a
singing voice corpus show that our proposed structure improves the naturalness
of the generated waveform. We also show that the speech waveforms with a pitch
outside of the training data range can be generated with more naturalness.
