Dictionary optimization and clustering for exemplar-based voice conversion

Exemplar-based voice conversion (VC) methods have several disadvantages: too many exemplars, phoneme mismatches, and low conversion efficiency. To solve these problems, this paper proposes a voice conversion method based on nonnegative matrix factorization (NMF) using Dictionary optimization and clustering, which applies low-resolution features instead of high-resolution features to construct dictionaries. Dictionary optimization based on minimizing cepstrum distortion selects some fitter exemplars from the original dictionary. Exemplar clustering divides the dictionary into multiple sub-dictionaries which have better representation based on feature parameters. The ARCTIC database is used for experiments. Results show that the proposed method can significantly improve the quality of converted speech while reducing the number of exemplars and improving efficiency.