Method and system for Gaussian probability data bit reduction and computation
First Claim
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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Abstract
Use of runtime memory may be reduced in a data processing algorithm that uses one or more probability distribution functions. Each probability distribution function may be characterized by one or more uncompressed mean values and one or more variance values. The uncompressed mean and variance values may be represented by α-bit floating point numbers, where α is an integer greater than 1. The probability distribution functions are converted 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 space than the uncompressed mean and/or variance values. Portions of the data processing algorithm can be performed with the compressed mean and variance values.
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Citations
34 Claims
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1. A speech recognition apparatus, comprising:
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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 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, 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. An apparatus for reducing use of runtime memory in a data processing algorithm that uses one or more Gaussian probability distribution functions for one or more different states of features xi that make up portions of an observed speech signal, wherein the Gaussian probability distribution functions include M Gaussian functions of N different observable features, each Gaussian function representing the probability distribution for a different one of M possible Gaussians, each Gaussian function being characterized by an uncompressed mean and an uncompressed variance, the apparatus comprising:
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means for processing the observed speech signal to determine the observable features for a time window; means for representing 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;means for converting 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 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; andmeans for determining a most likely state of the features from the M Gaussian functions with the compressed mean and variance values; and means for recognizing a recognizable pattern within the observed speech signal for the time window using the most likely state.
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15. A speech signal recognition method, comprising:
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observing N different features of an observed speech signal representing a real-world process; setting up M different probability distribution functions of the N different observable features with a signal processor, wherein each probability distribution function represents 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, processing the observed speech signal with the signal processor 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;converting the probability distribution functions with the signal processor 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,calculating a probability for each of the M possible Gaussians with the signal processor 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,determining a most probable state with the processor from the calculated probabilities for the M possible Gaussians; and recognizing a recognizable pattern within the observed speech signal with the signal processor for the time window using the most probable state. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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Specification