Improving the Quality of Standard GMM-Based Voice Conversion Systems by Considering Physically Motivated Linear Transformations

This paper presents a new method to train traditional voice conversion functions based on Gaussian mixture models, linear transforms and cepstral parameterization. Instead of using statistical criteria, this method calculates a set of linear transforms that represent physically meaningful spectral modifications such as frequency warping and amplitude scaling. Our experiments indicate that the proposed training method leads to significant improvements in the average quality of the converted speech with respect to traditional statistical methods. This is achieved without modifying the input/output parameters or the shape of the conversion function.