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It is well known that the key to voice conversion (VC) is to transform the spectral parameters of the source speaker to match that of the target speaker, where Gaussian mixture model (GMM) based statistical transformations have been commonly studied. However, these methods are performed using a frame-by-frame procedure, disregarding spectral envelope evolution and resulting in the significantly degraded...
In this paper, we present a new voice conversion method based on the state-space model (SSM). A modified version of the conventional SSM model is first proposed to describe the relationship between the source speech and the target speech in the spectral domain. Then the expectation maximum (EM) and variational Bayesian (VB) algorithms are individually employed to estimate the SSM parameters, resulting...
One of the most recent models for voice conversion is the classical LPC analysis-synthesis model combined with GMM, which aims to separate information from excitation and vocal tract and to learn the transformation rules with statistical methods. However, it does not work well as it is supposed to be due to the inaccuracy of the extracted feature information as well as the overly-smoothed spectral...
This paper presents an algorithm for voice conversion based on mixtures of linear transform (Ms-LT) which avoids the need for parallel training data inherent in conventional approaches. In maximum likelihood framework, the EM algorithm is used to compute the parameters of the conversion function. And the chirp z-transform is utilized to enhance the averaged spectral envelop due to the linear weighting...
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