A Study on Residual Prediction Techniques for Voice Conversion

Abstract

Several well-studied voice conversion techniques use line spectral frequencies as features to represent the spectral envelopes of the processed speech frames. In order to return to the time domain, these features are converted to linear predictive coefficients that serve as coefficients of a filter applied to an unknown residual signal. We compare several residual prediction approaches that have already been proposed in the literature dealing with voice conversion. We also present a novel technique that outperforms the others in terms of voice conversion performance and sound quality.