Improving segmental GMM based voice conversion method with target frame selection

In this paper, the voice conversion method based on segmental Gaussian mixture models (GMMs) is further improved by adding the module of target frame selection (TFS). Segmental GMMs are meant to replace a single GMM of a large number of mixture components with several voice-content specific GMMs each consisting of much fewer mixture components. In addition, TFS is used to find a frame, of spectral features near to the mapped feature vector, from the target-speaker frame pool corresponding to the segment class as the input frame belongs to. Both ideas are intended to alleviate the problem that the converted spectral envelopes are often over smoothed. To evaluate the performance of the two ideas mentioned, three voice conversion systems are constructed, and used to conduct listening tests. The results of the tests show that the system using the two ideas together can obtain much improved voice quality. In addition, the measured variance ratio (VR) values show that the system with the two ideas adopted also obtains the highest VR value.