Method and apparatus for pattern recognition employing the hidden Markov model

Claim 2

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, function value calculating means for generating a first calculation signal corresponding to a mapping of each pair C m and y, (C m ,y) into a signal u(y,m)εU=[a,b], where m=1, . . . , M, a,bεR 1 for 0≦a≦b, C={C 1 , C 2 , . . . , C M } is a set of signals against which the observation vector signal y is compared, yεR n is an element of n-dimensional Euclidean space, signal occurrence probability memory means for storing the occurrence probability of each signal of a set C, where said occurrence probabilities of signals in the set C are received and stored, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of logarithmic values of occurrence probabilities of signals in the set C or b) generating a third calculation signal representing the weighted arithmetic mean of said logarithmic values of occurrence probabilities of signals in the set C under the definition that the weighting coefficient for the m-th signal C m is u (y,m) obtained by said function value calculating means, wherein the calculation signal representing the weighted sum or weighted arithmetic mean is a degree of pattern recognition of the observation vector signal y.

Claim 3

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 1 wherein said sound is human speech.

Claim 4

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, function value calculating means for generating a first calculation signal corresponding to a mapping of each pair C m and y, (C m ,y) into a signal u(y,m)εU=[a,b], where m=1, . . . , M, a,b εR 1 for 0≦a≦b, C={C 1 , C 2 , . . . , C M } is a set of signals against which the observation vector signal y is compared, yεR n is an element of n-dimensional Euclidean space, signal occurrence probability memory means for storing the occurrence probability of each signal of a set C, where said occurrence probabilities of signals in the set C are received and stored, and power product calculating means for a) generating a second calculation signal representing the product of powers of occurrence probabilities of signals in the set C or b) generating a third calculation signal representing the weighted geometric mean of said occurrence probabilities of signals in the set C under the definition that the power or weighting coefficient for the m-th signal C m is u(y,m) obtained by said function value calculating means, wherein the product of power or the weighted geometric mean is a degree of pattern recognition of the observation vector signal y.

Claim 5

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, function value calculating means for generating a first calculation signal corresponding to a mapping of each pair C m and y, (C m ,y) into a signal u(y,m)εU=[a,b], where m=1, . . . , M, a,b εR 1 for 0≦a≦b, C={C 1 , C 2 , . . . , C M } is a set of signals against which the observation vector signal y is compared, yεR n is an element of n-dimensional Euclidean space, signal occurrence probability memory means for storing the occurrence probability of each signal of a set C, where said occurrence probabilities of signals in the set C are received and stored, and power product calculating means for a) generating a second calculation signal representing the product of powers of occurrence probabilities of signals in the set C or b) generating a third calculation signal representing the weighted geometric mean of said occurrence probabilities of signals in the set C under the definition that the power or weighting coefficient for the m-th signal C m is u(y,m) obtained by said function value calculating means, wherein the product of power or the weighted geometric mean is a degree of pattern recognition of the observation vector signal y.

Claim 6

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 3 wherein said sound source is human speech.

Claim 7

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, a signal cluster F m (m=1, . . . , M), where each signal cluster is obtained by clustering a training vector signal set, membership value calculating means for generating a first calculation signal representing the membership value of the observation vector signal y to each signal cluster F m (m=1, . . . , M), signal cluster occurrence probability memory means for storing the occurrence probability of each signal cluster, where said occurrence probabilities of signal clusters are received and stored, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of logarithmic values of said occurrence probabilities of signal clusters or b) generating a third calculation signal representing the weighted arithmetic mean of said logarithmic values of occurrence probabilities of signal clusters under the definition that the weighting coefficient for the m-th signal cluster F m is said membership value of y for F m obtained by said membership value calculating means, wherein the weighted sum or weighted arithmetic mean is a degree of pattern recognition of the observation vector signal y.

Claim 8

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, a signal cluster F m (m=1, . . . , M), where each signal cluster is obtained by clustering a training vector signal set, membership value calculating means for generating a first calculation signal representing the membership value of the observation vector signal y to each signal cluster F m (m=1, . . . , M), signal cluster occurrence probability memory means for storing the occurrence probability of each signal cluster, where said occurrence probabilities of signal clusters are received and stored, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of logarithmic values of said occurrence probabilities of signal clusters or b) generating a third calculation signal representing the weighted arithmetic mean of said logarithmic values of occurrence probabilities of signal clusters under the definition that the weighting coefficient for the m-th signal cluster F m is said membership value of y for F m obtained by said membership value calculating means, wherein the weighted sum or weighted arithmetic mean is a degree of pattern recognition of the observation vector signal y.

Claim 9

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 5, wherein the calculation signal representing the weighted sum is generated on the closest K signal clusters of the observation vector signal y, to a predetermined K.

Claim 10

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 5, wherein the calculation signal representing the weighted sum is generated on the signal clusters of which membership value of the observation vector signal y is more than a predetermined threshold.

Claim 11

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 5 wherein said sound is human speech.

Claim 12

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, a signal cluster F m (m=1, . . . , M), where each signal cluster is obtained by clustering the training vector signal set, membership value calculating means for generating a first calculation signal representing the membership value of the observation vector signal y to each signal cluster F m (m=1, . . . , M), signal cluster occurrence probability memory means for storing the occurrence probability of each signal cluster, where a priori occurrence probabilities of signal clusters are received and stored, and power product calculating means for a) generating a second calculation signal representing the product of powers of said a priori occurrence probabilities of signal clusters or b) generating a third calculation signal representing the weighted geometric mean of said a priori occurrence probabilities of signal clusters under the definition that the power or weighting coefficient for the m-th signal cluster F m is said membership value of y for F m obtained by said membership value calculating means, wherein the product of powers or the weighted geometric mean is a degree of pattern recognition of the observation vector signal y.

Claim 13

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, a signal cluster F m (m=1, . . . , M), where each signal cluster is obtained by clustering the training vector signal set, membership value calculating means for generating a first calculation signal representing the membership value of the observation vector signal y to each signal cluster F m (m=1, . . . , M), signal cluster occurrence probability memory means for storing the occurrence probability of each signal cluster, where a priori occurrence probabilities of signal clusters are received and stored, and power product calculating means for a) generating a second calculation signal representing the product of powers of said a priori occurrence probabilities of signal clusters or b) generating a third calculation signal representing the weighted geometric mean of said a priori occurrence probabilities of signal clusters under the definition that the power or weighting coefficient for the m-th signal cluster F m is said membership value of y for F m obtained by said membership value calculating means, wherein the product of powers or the weighted geometric mean is a degree of pattern recognition of the observation vector signal y.

Claim 14

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 9, wherein the calculation signal representing the product of powers is generated on the closest K signal clusters of the observation vector signal y, to a predetermined K.

Claim 15

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 9, wherein the calculation signal representing the product of powers is generated on the signal clusters of which membership value of the observation vector signal y is more than a predetermined threshold.

Claim 16

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 9 wherein said sound is human speech.

Claim 17

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, source signal occurrence probability memory means for storing a priori occurrence probability of each source signal, where said a priori occurrence probabilities of source signals are received and stored, a posteriori probability calculating means for generating a first calculation signal representing the a posteriori probability of each of said source signals D 1 , . . . , D m for an observation vector signal y, where each source signal generates said observation vector y according to a probability density defined for the source signal, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of said a priori occurrence probabilities of source signals or b) generating a third calculation signal representing the weighted arithmetic mean of logarithmic value of said a priori occurrence probabilities of source signals under the definition that the weighting coefficient for the m-th source signal is defined by the a posteriori probability of the source signal C m calculated by said a posteriori probability calculating means, wherein the calculation signal representing the weighted sum or the weighted arithmetic mean is a degree of pattern recognition of the observation vector signal y.

Claim 18

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, source signal occurrence probability memory means for storing a priori occurrence probability of each source signal, where said a priori occurrence probabilities of source signals are received and stored, a posteriori probability calculating means for generating a first calculation signal representing the a posteriori probability of each of said source signals D 1 , . . . , D m for an observation vector signal y, where each source signal generates said observation vector y according to a probability density defined for the source signal, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of said a priori occurrence probabilities of source signals or b) generating a third calculation signal representing the weighted arithmetic mean of logarithmic value of said a priori occurrence probabilities of source signals under the definition that the weighting coefficient for the m-th source signal is defined by the a posteriori probability of the source signal C m calculated by said a posteriori probability calculating means, wherein the calculation signal representing the weighted sum or the weighted arithmetic mean is a degree of pattern recognition of the observation vector signal y.

Claim 19

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 13, wherein the source signal generates vector signals contained in each signal cluster obtained by clustering the training vector signal set, and the a posteriori probability to the observation vector signal of the source signal is the membership value of the observation vector signal y to the signal cluster.

Claim 20

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 13 wherein said sound is human speech.

Claim 21

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, source signal occurrence probability memory means for storing a priori occurrence probability of each source signal, where said a priori occurrence probabilities of source signals are received and stored, a posteriori probability calculating means for generating a first calculation signal representing the a posteriori probability of each of said source signals D 1 , . . . , D m for the observation vector signal y, where each source signal generates said observation vector signal y according to a probability density defined for the source signal, and power product calculating means for a) generating a second calculation signal representing the product of powers of said a priori occurrence probabilities of source signals or b) generating a third calculation signal representing the weighted geometric mean of said a priori occurrence probabilities of source signals under the definition that the power or weighting coefficient for the m-th source signal is defined by the a posteriori probability of the source signal C m calculated by said a posteriori probability calculating means, wherein the calculation signal representing the product of power or the weighted geometric mean is a degree of pattern recognition of the observation vector signal y.

Claim 22

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, source signal occurrence probability memory means for storing a priori occurrence probability of each source signal, where said a priori occurrence probabilities of source signals are received and stored, a posteriori probability calculating means for generating a first calculation signal representing the a posteriori probability of each of said source signals D 1 , . . . , D m for the observation vector signal y, where each source signal generates said observation vector signal y according to a probability density defined for the source signal, and power product calculating means for a) generating a second calculation signal representing the product of powers of said a priori occurrence probabilities of source signals or b) generating a third calculation signal representing the weighted geometric mean of said a priori occurrence probabilities of source signals under the definition that the power or weighting coefficient for the m-th source signal is defined by the a posteriori probability of the source signal C m calculated by said a posteriori probability calculating means, wherein the calculation signal representing the product of power or the weighted geometric mean is a degree of pattern recognition of the observation vector signal y.

Claim 23

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 16, wherein the source signal generates vector signals contained in each signal cluster obtained by clustering a training vector signal set, and the a posteriori probability to the observation vector signal y of the source signal is the membership value of the observation vector signal y to the signal cluster.

Claim 24

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 16 wherein said sound is human speech.

Claim 25

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, signal occurrence degree memory means for storing the degree of occurrence of C m in the conditions of predetermined S n , for a first set S={S 1 , S 2 , . . . ,S N } composed of N signals, and a second set C={C 1 , C 2 , . . . , C M } composed of M signals, first function calculating means for generating a first calculation signal representing a first function value expressing the strength of relation between a pattern set Z generated from information source A generating time series signals and the signal S n , second function calculating means for generating a second calculation signal representing a second function value expressing the strength of relation between the observation pattern signal y and signal C m , and observation pattern occurrence degree calculating means for generating a third calculation signal representing the possibility of occurrence of the observation pattern signal y from the information source A, from the occurrence degree of each signal of C stored in the signal occurrence degree memory means and the first function value and second function value.

Claim 26

A computer implemented signal processing apparatus used for a sound recognition apparatus comprising: means for receiving a signal representing a sound and converting the signal representing the sound to a time series observation vector signal y, signal occurrence degree memory means for storing the degree of occurrence of C m in the conditions of predetermined S n , for a first set S={S 1 , S 2 , . . . ,S N } composed of N signals, and a second set C={C 1 , C 2 , . . . , C M } composed of M signals, first function calculating means for generating a first calculation signal representing a first function value expressing the strength of relation between a pattern set Z generated from information source A generating time series signals and the signal S n , second function calculating means for generating a second calculation signal representing a second function value expressing the strength of relation between the observation pattern signal y and signal C m , and observation pattern occurrence degree calculating means for generating a third calculation signal representing the possibility of occurrence of the observation pattern signal y from the information source A, from the occurrence degree of each signal of C stored in the signal occurrence degree memory means and the first function value and second function value.

Claim 27

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 19, wherein the observation pattern occurrence degree calculating means a) generates a first calculation signal representing the weighted sum of logarithmic values of occurrence probabilities of signal in the set C or b) generates a second calculation signal representing the weighted arithmetic mean of said occurrence probabilities in the set C, where said occurrence probabilities of signal in the set C are received and stored in said signal occurrence degree memory means, where weighting coefficients used to obtain said weighted sum or said weighted arithmetic mean are the product of first function values and second function values, wherein said calculation signal representing the weighted sum or said weighted arithmetic mean is a degree of pattern recognition of the observation pattern signal y.

Claim 28

A computer implemented signal processing apparatus used for a sound recognition apparatus of claim 19, wherein the observation pattern occurrence degree calculating means a) generates a first calculation signal representing the product of powers of occurrence probabilities of signals in the set C or b) generates a second calculation signal representing the weighted geometric mean of occurrence probabilities of signals in the set C, where said occurrence probabilities of signals in the set C are memorized in said signal occurrence degree memory means, where said power or weighting coefficients used to obtain said product of powers or said weighted geometric mean are products of the first function values and the second function values, wherein said calculation signal representing the product of powers or said weighted geometric mean is the occurrence degree of the observation pattern signal y.

Claim 29

A computer implemented signal processing apparatus used for a sound recognition apparatus according to claim 19 wherein said sound is human speech.

Claim 30

A computer implemented signal processing apparatus comprising: feature extraction means for receiving a sound and converting the sound to a time series observation vector signal y, function value calculating means for generating a first calculation signal corresponding to a mapping of each pair C m and y, (C m ,y) into a signal u(y,m)εU=[a,b], where m=1, . . . , M, a,b εR 1 for 0≦a≦b, C={C 1 , C 2 , . . . , C M } is a set of signals against which the observation vector signal y is compared, yεR n is an element of n-dimensional Euclidean space, signal occurrence probability memory means for storing the occurrence probability of each signal of a set C, where said occurrence probabilities of signals in the set C are received and stored, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of logarithmic values of occurrence probabilities of signals in the set C or b) generating a third calculation signal representing the weighted arithmetic mean of said logarithmic value of occurrence probabilities of signals in the set C under the definition that the weighting coefficient for the m-th signal C m is u(y,m) obtained by said function value calculating means, and decision means for generating a recognized word corresponding to the sound using the calculation signal representing the weighted sum or weighted arithmetic mean.

Claim 31

A computer implemented signal processing apparatus comprising: feature extraction means for receiving a sound and converting the sound to a time series observation vector signal y, function value calculating means for generating a first calculation signal corresponding to a mapping of each pair C m and y, (C m ,y) into a signal u(y,m)εU=[a,b], where m=1, . . . , M, a,b εR 1 for 0≦a≦b, C={C 1 , C 2 , . . . , C M } is a set of signals against which the observation vector signal y is compared, yεR n is an element of n-dimensional Euclidean space, signal occurrence probability memory means for storing the occurrence probability of each signal of a set C, where said occurrence probabilities of signals in the set C are received and stored, and weighted sum calculating means for a) generating a second calculation signal representing the weighted sum of logarithmic values of occurrence probabilities of signals in the set C or b) generating a third calculation signal representing the weighted arithmetic mean of said logarithmic value of occurrence probabilities of signals in the set C under the definition that the weighting coefficient for the m-th signal C m is u(y,m) obtained by said function value calculating means, and decision means for generating a recognized word corresponding to the sound using the calculation signal representing the weighted sum or weighted arithmetic mean.