  Neural waveform models such as WaveNet have demonstrated better performance
than conventional vocoders for statistical parametric speech synthesis. As an
autoregressive (AR) model, WaveNet is limited by a slow sequential waveform
generation process. Some new models that use the inverse-autoregressive flow
(IAF) can generate a whole waveform in a one-shot manner. However, these
IAF-based models require sequential transformation during training, which
severely slows down the training speed. Other models such as Parallel WaveNet
and ClariNet bring together the benefits of AR and IAF-based models and train
an IAF model by transferring the knowledge from a pre-trained AR teacher to an
IAF student without any sequential transformation. However, both models require
additional training criteria, and their implementation is prohibitively
complicated.
  We propose a framework for neural source-filter (NSF) waveform modeling
without AR nor IAF-based approaches. This framework requires only three
components for waveform generation: a source module that generates a sine-based
signal as excitation, a non-AR dilated-convolution-based filter module that
transforms the excitation into a waveform, and a conditional module that
pre-processes the acoustic features for the source and filer modules. This
framework minimizes spectral-amplitude distances for model training, which can
be efficiently implemented by using short-time Fourier transform routines.
Under this framework, we designed three NSF models and compared them with
WaveNet. It was demonstrated that the NSF models generated waveforms at least
100 times faster than WaveNet, and the quality of the synthetic speech from the
best NSF model was better than or equally good as that from WaveNet.
