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Neural source-filter (NSF) waveform models generate speech waveforms bymorphing sine-based source signals through dilated convolution in the timedomain. Although the sine-based source signals help the NSF models to producevoiced sounds with specified pitch, the sine shape may constrain the generatedwaveform when the target voiced sounds are less periodic. In this paper, wepropose a more flexible source signal called cyclic noise, a quasi-periodicnoise sequence given by the convolution of a pulse train and a static randomnoise with a trainable decaying rate that controls the signal shape. We furtherpropose a masked spectral loss to guide the NSF models to produce periodicvoiced sounds from the cyclic noise-based source signal. Results from alarge-scale listening test demonstrated the effectiveness of the cyclic noiseand the masked spectral loss on speaker-independent NSF models incopy-synthesis experiments on the CMU ARCTIC database.

Using Cyclic Noise as the Source Signal for Neural Source-Filter-based Speech Waveform Model