Sequential voice conversion using grid-based approximation

Common voice conversion methods are based on Gaussian Mixture Modeling (GMM), which requires exhaustive training (typically lasting hours), often leading to ill-conditioning if the dataset used is too small. We propose a new conversion method that is trained in seconds, using either small or large scale datasets. The proposed Grid-Based (GB) method is based on sequential Bayesian tracking, by which the conversion process is expressed as a sequential estimation problem of tracking the target spectrum based on the observed source spectrum. The converted MFCC vectors are sequentially evaluated using a weighted sum of the target training set used as grid-points. To improve the perceived quality of the synthesized signals, we use a post-processing block for enhancing the global variance. Objective and subjective evaluations show that the enhanced-GB method is comparable to classic GMM-based methods, in terms of quality, and comparable to their enhanced versions, in terms of individuality.