  This paper proposes a WaveNet-based neural excitation model (ExcitNet) for
statistical parametric speech synthesis systems. Conventional WaveNet-based
neural vocoding systems significantly improve the perceptual quality of
synthesized speech by statistically generating a time sequence of speech
waveforms through an auto-regressive framework. However, they often suffer from
noisy outputs because of the difficulties in capturing the complicated
time-varying nature of speech signals. To improve modeling efficiency, the
proposed ExcitNet vocoder employs an adaptive inverse filter to decouple
spectral components from the speech signal. The residual component, i.e.
excitation signal, is then trained and generated within the WaveNet framework.
In this way, the quality of the synthesized speech signal can be further
improved since the spectral component is well represented by a deep learning
framework and, moreover, the residual component is efficiently generated by the
WaveNet framework. Experimental results show that the proposed ExcitNet
vocoder, trained both speaker-dependently and speaker-independently,
outperforms traditional linear prediction vocoders and similarly configured
conventional WaveNet vocoders.
