SPAM and full covariance for speech recognition

Abstract

The Subspace Precision and Mean model (SPAM) is a way of representing Gaussian precision and mean values in a reduced dimension. This paper presents some large vocabulary experiments with SPAM and introduces an efficient way to optimize the SPAM basis. We present experiments comparing SPAM, diagonal covariance and full covariance models on a large vocabulary task. We also give explicit formulae for an implementation of SPAM.