Rapid likelihood calculation of subspace clustered Gaussian components

Abstract

In speech recognition systems, computing the likelihoods of the acoustic models is an intensive task. One approach to reduce this cost is to use subspace distributed clustering HMM. Here individual Gaussian components are stored as indices to, and their likelihoods computed from, a set of subspace Gaussian components. This paper examines a scheme for reducing the computational cost of the likelihood calculation when such an HMM system is used. The proposed method identifies and stores frequently occurring partial sums called meta-atom elements and thus avoids computing them repeatedly. The resultant savings in the number of additions is 50% when all Gaussian components are computed or 20% when a Gaussian selection scheme is used.