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

Reducing one-to-many problem in Voice Conversion by equalizing the formant locations using dynamic frequency warping