Voice Conversion Based on Hybrid SVR and GMM - SONG, Peng; JIN, Yun; ZHAO, Li; ZOU, Cairong (Archives of Acoustics, 2012)
A novel VC (voice conversion) method based on hybrid SVR (support vector regression) and GMM(Gaussian mixture model) is presented in the paper, the mapping abilities of SVR and GMM are exploitedto map the spectral features of the source speaker to those of target ones. A new strategy of F0 transfor-mation is also presented, the F0s are modeled with spectral features in a joint GMM and predicted fromthe converted spectral features using the SVR method. Subjective and objective tests are carried out toevaluate the VC performance; experimental results show that the converted speech using the proposedmethod can obtain a better quality than that using the state-of-the-art GMM method. Meanwhile, a VCmethod based on non-parallel data is also proposed, the speaker-specific information is investigated us-ing the SVR method and preliminary subjective experiments demonstrate that the proposed method isfeasible when a parallel corpus is not available.