Constrained probabilistic subspace maps applied to speech enhancement

This paper presents a probabilistic algorithm that extracts a mapping between two subspaces by representing each subspace as a collection of states. In many cases, the data is a time series with temporal constraints. This paper suggests a method to impose these temporal constraints on the transitions between the states of the subspace. This probabilistic model has been successfully applied to the problem of speech enhancement and improves the performance of a Wiener filter by providing robust estimates of a priori SNR.