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