Vector Processor Method for Computing Multiple Multivariate Gaussian Probabilities
Original Publication Date: 1989-Jan-01
Included in the Prior Art Database: 2005-Jan-26
In many speech recognition systems, it is necessary to compute the probability of an observation over many nultivariate Gaussian probability density functions. A vector processor algorithm is described for computing multiple multivariate Gaussians that provides substantial performance improvements over scalar computation even when the dimensionality of the observation vector is small. 1. Background Many speech recognition algorithms [1,2,3] model an input parameter vector y as having an underlying Gaussian distribution of the form where N is the dimension of y, m is the mean vector, and is a (positive definite) covariance matrix. Many of these algorithms require computation of y for many different values of m and . Typically, one is able to deal with log p(y) since the log is a monotonic transformation.