Training a parametric-based logF0 model with the minimum generation error criterion

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This paper describes an approach for improving a statistical parametric-based logF0 model using minimum-generationerror (MGE) training. Compared with the previous scheme based on decision tree clustering, MGE allows the minimisation of the error in the generated logF0 to take into account not only each cluster by itself, but also the way in which the clusters interact with each other in the generation of the F0 over the whole sentence. Moreover, the “weights” of each component of the model, which previously were adjusted manually, are optimized automatically by the MGE training during the re-estimation of the model covariances. Objective evaluation indicated that, although the logF0 contours generated by the models trained with MGE have approximately the same root mean square error and correlation factor as those generated with the baseline models, they present a higher dynamic range. The subjective evaluation shows a small but significant preference for the system trained with MGE.