On the use of an iterative estimation of continuous probabilistic transforms for voice conversion

Voice conversion is a technique that modifies a source speaker´s speech to be perceived as if a target speaker had spoken it. Among the algorithms of conversion published in the literature, the techniques using GMM are nowadays the reference. In this paper, we focus on a new technique for estimating parameters of the conversion function. We show that it is possible to determinate these parameters without statistical estimation using the classical Expectation Minimization (EM) algorithm. We propose a new method for determining the conversion function parameters. The technique proposed is based on an iterative statistical refinement algorithm working directly from data. The consequence of this strategy is that the estimation of the conversion function is very fast.