Improving of Segmental LMR-Mapping Based Voice Conversion Method

Spectral over-smoothing is still observable in the converted spectral envelope when linear multivariate regression (LMR) based spectrum mapping is adopted to convert voice. Therefore, in this paper, we study to place a histogram-equalization (HEQ) module immediately before LMR based mapping and to place a target frame selection (TFS) module immediately after LMR based mapping. These two modules are intended to promote the quality of the converted voice. Here, HEQ processing includes the two steps: (a) transform discrete cepstral coefficients (DCC) into principal component analysis (PCA) coefficients; (b) transform PCA coefficients into cumulated density function (CDF) coefficients. As to TFS, an input frame is first processed to obtain its converted DCC and its segment-class number. Then, the group of target-speaker frames corresponding to the same segment-class number is searched to find a target frame whose DCC are sufficiently close to the converted DCC. Next, the converted DCC are replaced by the DCC of the target frame found. In experimental evaluation, the outside parallel sentences (not used in model-parameter training) are used to measure average cepstral distances (ACD) between the converted DCC and the target DCC. When the HEQ module is added, the value of ACD would be increased a little. Furthermore, the value of ACD would be apparently increased when the TFS module is added. Nevertheless, according to the measured VR (variance ratio) values and the scores of subjective listening tests, the quality of the converted voice will become better when HEQ is added, and become much better when TFS is added. As to the reasons for why the measured ACD values and the perceived converted-voice qualities are inconsistent, we have found one possible cause which can explain why this inconsistency may occur.