Deleted Smoothing of HMM Parameters in Speech Recognition

Abstract

A technique for smoothing hidden Markov model parameters based on the concepts of deleted estimation and probabilistic mapping is proposed. The proposed algorithm is closely related to deleted interpolation in its approach and is shown to yield higher recognition rate than the distance-based smoothing and co-occurrence smoothing methods.