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

Abstract: 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.