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Method and system for Gaussian probability data bit reduction and computation

  • US 7,970,613 B2
  • Filed: 11/12/2005
  • Issued: 06/28/2011
  • Est. Priority Date: 11/12/2005
  • Status: Active Grant
First Claim
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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 variancewherein 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 2β



    1, if the quantity is greater than 2β



    1, wherein the function is equal to −

    (2β



    1) if the quantity is less than −

    (2β



    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, andwherein the signal processor is configured to recognize a recognizable pattern within the observed speech signal for the time window using the most probable state.

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