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

Training a Neural Speech Waveform Model using Spectral Losses of Short-Time Fourier Transform and Continuous Wavelet Transform