Mixtures of inverse covariances

We introduce a model that approximates full and block-diagonal covariances in a Gaussian mixture, while reducing significantly both the number of parameters to estimate and the computations required to evaluate the Gaussian likelihoods. The inverse covariance of each Gaussian is expressed as a mixture of a small set of prototype matrices. Estimation of both the mixture weights and the prototypes is performed using maximum likelihood estimation. Experiments on a variety of speech recognition tasks show that this model significantly outperforms a diagonal covariance model, while using the same number of Gaussian-dependent parameters.