Local Linear Transformation for Voice Conversion
Many popular approaches to spectral conversion involve linear transformations determined for particular acoustic classes and compute the converted result as 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 for the class of methods involving linear transformations. Perceptual results indicate a clear preference for the proposed scheme.Keywords
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- To be published in 2012.