Observation-model error compensation for enhanced spectral envelope transformation in voice conversion

A strategy to enhance the signal quality and naturalness was designed for performing probabilistic spectral envelope transformation in voice conversion. The existing modeling error of the probabilistic mixture to represent the observed envelope features is translated generally as an averaging of the information in the spectral domain, resulting in over-smoothed spectra. Moreover, a transformation based on poorly modeled features might not be considered reliable. Our strategy consists of a novel definition of the spectral transformation to compensate the effect of both over-smoothing and poor modeling. The results of an experimental evaluation show that the perceived naturalness of converted speech was enhanced.