Voice Conversion Without Parallel Speech Corpus Based on Mixtures of Linear Transform

This paper presents an algorithm for voice conversion based on mixtures of linear transform (Ms-LT) which avoids the need for parallel training data inherent in conventional approaches. In maximum likelihood framework, the EM algorithm is used to compute the parameters of the conversion function. And the chirp z-transform is utilized to enhance the averaged spectral envelop due to the linear weighting. The proposed voice conversion system is evaluated using both objective and subjective measures. The experimental results demonstrate that our approach is capable of effectively transforming speaker identity and can achieve comparable results of the conventional methods where a parallel corpus exists.