  Recently, we proposed short-time Fourier transform (STFT)-based loss
functions for training a neural speech waveform model. In this paper, we
generalize the above framework and propose a training scheme for such models
based on spectral amplitude and phase losses obtained by either STFT or
continuous wavelet transform (CWT), or both of them. Since CWT is capable of
having time and frequency resolutions different from those of STFT and is cable
of considering those closer to human auditory scales, the proposed loss
functions could provide complementary information on speech signals.
Experimental results showed that it is possible to train a high-quality model
by using the proposed CWT spectral loss and is as good as one using STFT-based
loss.
