Local linear transformation for voice conversion
Victor Popa, Hanna Silén, Jani Nurminen, Moncef Gabbouj · 2012
Many popular approaches to spectral conversion involve linear transformations determined for particular acoustic classes and compute the converted result as a linear combination between different local transformations in an attempt to ensure a continuous conversion. These methods often produce over-smoothed spectra and parameter tracks. The proposed method computes an individual linear transformation for every feature vector based on a small neighborhood in the acoustic space thus preserving local details. The method effectively reduces the over-smoothing by eliminating undesired contributions from acoustically remote regions. The method is evaluated in listening tests against the well-known Gaussian Mixture Model based conversion, representative of the class of methods involving linear transformations. Perceptual results indicate a clear preference for the proposed scheme.