Victor Popa

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 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.

A Study of Bilinear Models in Voice Conversion

This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a flexible representation suitable for voice conversion and facilitates the use of efficient training algorithms based on singular value decomposition. In a contextual approach (bilinear) models are trained on subsets of the training data selected on the fly at conversion time depending on the characteristics of the feature vector to be converted. The performance of bilinear models and context modeling is evaluated in objective and perceptual tests by comparison with the popular GMM-based voice conversion method for several sizes and different types of training data.