Combining local PCA and radial basis function networks for speaker normalization

Complex multidimensional data may naturally require the decomposition of a regression/classification problem over local regions. Moreover, both global and local anisotropy can be present. We propose to address both problems with a flexible neural network structure embedding data quantization and coordinate transformations. The solution is applied in this paper to speaker normalization. The spectral mapping is realized as a weighted superposition of local neural mappings, estimated between subregions of a new speaker acoustic space and that of a reference speaker, combined with global and local space transformations. The local mappings are realized using the generalized resource allocating network model, a general radial basis function scheme that allows recursive allocation of kernels. The space transformations are based upon projections over the principal components, separately estimated for the global space and for the local subregions of the input and output acoustic spaces