  Neural waveform models such as the WaveNet are used in many recent
text-to-speech systems, but the original WaveNet is quite slow in waveform
generation because of its autoregressive (AR) structure. Although faster non-AR
models were recently reported, they may be prohibitively complicated due to the
use of a distilling training method and the blend of other disparate training
criteria. This study proposes a non-AR neural source-filter waveform model that
can be directly trained using spectrum-based training criteria and the
stochastic gradient descent method. Given the input acoustic features, the
proposed model first uses a source module to generate a sine-based excitation
signal and then uses a filter module to transform the excitation signal into
the output speech waveform. Our experiments demonstrated that the proposed
model generated waveforms at least 100 times faster than the AR WaveNet and the
quality of its synthetic speech is close to that of speech generated by the AR
WaveNet. Ablation test results showed that both the sine-wave excitation signal
and the spectrum-based training criteria were essential to the performance of
the proposed model.
