Objective evaluation of the Dynamic Model Selection method for spectral voice conversion

Abstract

Spectral voice conversion is usually performed using a single model selected in order to represent a tradeoff between goodness of fit and complexity. Recently, we proposed a new method for spectral voice conversion, called Dynamic Model Selection (DMS), in which we assumed that the model topology may change over time, depending on the source acoustic features. In this method a set of models with increasing complexity is considered during the conversion of a source speech signal into a target speech signal. During the conversion, the best model is dynamically selected among the models in the set, according to the acoustical features of each source frame. In this paper, we present an objective evaluation demonstrating that this new method improves the conversion by reducing the transformation error compared to methods based on an single model.