2005 | OriginalPaper | Buchkapitel
Voice Conversion Based on Weighted Least Squares Estimation Criterion and Residual Prediction from Pitch Contour
verfasst von : Jian Zhang, Jun Sun, Beiqian Dai
Erschienen in: Affective Computing and Intelligent Interaction
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This paper describes an enhanced system for more efficient voice conversion. A weighted LMSE (Least Mean Squared Error) criterion is adopted, instead of conventional LMSE, for the spectral conversion function training. In addition, a short-term pitch contour mapping algorithm together with a new residual codebook formed from pitch contour is presented. Informal listening tests prove that convincing voice conversion is achieved while maintaining high speech quality. Evaluations by objective tests also show that the proposed system reduces speaker individual discrimination compared with the baseline system in LPC based analysis/synthesis framework.