Optimal learning of P-Layer additive F0 models with cross-validation

In this paper, we present the derivation of the backfitting training algorithms for generic p-layer additive F0 models for arbitrary positive integer p. We have presented the special cases of the algorithms with p = 2 and p = 3 that have been successfully applied to the modelings of Japanese and English F0 contours, whereas the derivation of the algorithm was presented only for the two-layer case. The additive F0 model have smoothing parameters that establish a trade-off between the fit to the training data and the smoothness of the fitted curves, which have been all set to unity in the previous works. In this paper, we also present an optimal approach to set the values of these parameters using cross validation. We performed the training using the Boston University Radio News Corpus and confirmed the effectiveness of the proposed method.