Robust voice conversion methods

Several problems in the training and transformation stages of voice conversion algorithms cause reduction in the output quality. This study focuses on the improvement of output quality in STASC based voice conversion and proposes five new methods. Robust end-point detection, pre-emphasis and spectral equalization are the three new methods that are employed in the training stage. The fourth method employs confidence measures for eliminating source and target HMM states that are significantly different in terms of duration, vocal tract spectrum, pitch, and energy. The last method focuses on the improvement of the pitch detection method. The optimal parameters of an autocorrelation based pitch detector are determined for male and female speakers separately with detailed analysis. The f/sub 0/ values obtained from electro-glottograph signals are used as the reference. The algorithm that employs the proposed methods is compared with STASC in subjective listening tests. The similarity to the target voice is increased by 23.0% and the subjective quality by 28.8% with the new methods.