  In this study, we investigate a solution to reduce the effect of one-to-many
problem in voice conversion. One-to-many problem in VC happens when two very
similar speech segments in source speaker have corresponding speech segments in
target speaker that are not similar to each other. As a result, the mapper
function usually over-smoothes the generated features in order to be similar to
both target speech segments. In this study, we propose to equalize the formant
location of source-target frame pairs using dynamic frequency warping in order
to reduce the complexity. After the conversion, another dynamic frequency
warping is further applied to reverse the effect of formant location
equalization during the training. The subjective experiments showed that the
proposed approach improves the speech quality significantly.
