LSF mapping for voice conversion with very small training sets

To make voice conversion usable in practical applications, the number of training sentences should be minimized. With traditional Gaussian mixture model (GMM) based techniques small training sets lead to over-fitting and estimation problems. We propose a new approach for mapping line spectral frequencies (LSFs) representing the vocal tract. The idea is based on inherent intra-frame correlations of LSFs. For each target LSF, a separate GMM is used and only the source and target LSF elements best correlating with the current LSF are used in training. The proposed method is evaluated both objectively and in listening tests, and it is shown that the method outperforms the conventional GMM approach especially with very small training sets.