1. A speech recognition apparatus, comprising: a signal processor configured to observe N different features of an observed speech signal and set up M different probability distribution functions of the N different observable features, each probability distribution function representing a probability of a different one of M possible Gaussians of a portion of the observed speech signal, wherein each Gaussian is characterized by a corresponding uncompressed mean a corresponding uncompressed variance wherein the signal processor is configured to process the observed signal to determine the observable features for a time window and represent the one or more different states of the features with the M Gaussian probability distribution functions, wherein the uncompressed mean and variance values are represented by α-bit floating point numbers, where α is an integer greater than 1; wherein the signal processor is configured to convert the probability distribution functions to compressed probability functions having compressed mean and/or variance values represented as β-bit integers, where β is less than α, whereby the compressed mean and/or variance values occupy less memory than the uncompressed mean and/or variance values, wherein the signal processor is configured to calculate a probability for each of the M possible Gaussians using the compressed probability functions wherein each compressed mean value is equal to a function of a quantity, wherein the quantity is product of a difference between the uncompressed variance and a centroid of the means for a given observable feature for all possible Gaussians with a variance for the given observable feature for all possible Gaussians, wherein the function is equal to 2 β −1, if the quantity is greater than 2 β −1, wherein the function is equal to −(2 β −1) if the quantity is less than −(2 β −1), and wherein the function is equal to a fixed point representation of the quantity otherwise, wherein the signal processor is configured to determine a most probable state from the calculated probabilities for the M possible Gaussians, and wherein the signal processor is configured to recognize a recognizable pattern within the observed speech signal for the time window using the most probable state.