Sparse probabilistic state mapping and its application to speech bandwidth expansion

In this paper we present a probabilistic algorithm that extracts a mapping between two subspaces by representing each subspace as a collection of states. An arbitrary increase in number of states results in over-fitting the training data without exploring the underlying structure of the map. This paper suggests a method to impose sparsity constraints on the state map by using entropic priors. This probabilistic model is applied to the problem of artificial bandwidth expansion that involves estimating the missing frequency components (3.7 - 8 kHz and 0 - 0.3 kHz) of speech given the narrowband speech signal (0.3 - 3.7 kHz).