Text-independent F0 transformation with non-parallel data for voice conversion

In voice conversion, a simple frame-level mean and variance normalization is typically used for fundamental frequency (F0) transformation, which is text-independent and requires no parallel training data. Some advanced methods transform pitch contours instead, but require either parallel training data or syllabic annotations. We propose a method which retains the simplicity and text-independence of the frame-level conversion while yielding high-quality conversion. We achieve these goals by (1) introducing a text-independent tri-frame alignment method, (2) including delta features of F0 into Gaussian mixture conversion and (3) reducing the well-known GMM oversmoothing effect by F0 histogram equalization. Our objective and subjective experiments on the CMU-Arctic corpus indicate improvements over both the mean/variance normalization and the baseline GMM conversion.