A GMM based residual prediction method for voice conversion

The purpose of a voice conversion (VC) system is to change the perceived speaker identity of a speech signal to sound as if a target speaker had spoken it. In this paper, we propose a residual prediction method for the spectral detail transformation component of a VC system. The algorithm described here is based on the LPC analysis/synthesis framework, and achieves residual prediction from LPC parameters during voiced speech. This step consists of a GMM based LPC parameter classifier and a LPC residual codebook. The predicted residual is then combined with all-pole LPC spectrum to synthesize speech signal. Several aspects of this residual prediction method, including the validation of the codebook and the performance of the coded speech are tested using objective measures. The converted speech is found nearly indistinguishable from the target speaker´s individuality in informal listening tests.