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In this paper, we propose computationally efficient and high-quality methodsfor statistical voice conversion (VC) with direct waveform modification basedon spectral differentials. The conventional method with a minimum-phase filterachieves high-quality conversion but requires heavy computation in filtering.This is because the minimum phase using a fixed lifter of the Hilbert transformoften results in a long-tap filter. One of our methods is a data-driven methodfor lifter training. Since this method takes filter truncation into account intraining, it can shorten the tap length of the filter while preservingconversion accuracy. Our other method is sub-band processing for extending theconventional method from narrow-band (16 kHz) to full-band (48 kHz) VC, whichcan convert a full-band waveform with higher converted-speech quality.Experimental results indicate that 1) the proposed lifter-training method fornarrow-band VC can shorten the tap length to 1/16 without degrading theconverted-speech quality and 2) the proposed sub-band-processing method forfull-band VC can improve the converted-speech quality than the conventionalmethod.

Lifter Training and Sub-band Modeling for Computationally Efficient and High-Quality Voice Conversion Using Spectral Differentials