Dynamic programming approach to voice transformation

This paper presents a voice transformation algorithm which modifies the speech of a source speaker such that it is perceived as if spoken by a target speaker. A novel method which is based on dynamic programming approach is proposed. The designed system obtains speaker-specific codebooks of line spectral frequencies (LSFs) for both source and target speakers. Those codebooks are used to train a mapping histogram matrix, which is used for LSF transformation from one speaker to the other. The baseline system uses the maxima of the histogram matrix for LSF transformation. The shortcomings of this system, which are the limitations of the target LSF space and the spectral discontinuities due to independent mapping of subsequent frames, have been overcome by applying the dynamic programming approach. Dynamic programming approach tries to model the long-term behaviour of LSFs of the target speaker, while it is trying to preserve the relationship between the subsequent frames of the source LSFs, during transformation. Both objective and subjective evaluations have been conducted and it has been shown that dynamic programming approach improves the performance of the system in terms of both the speech quality and speaker similarity.