Method and apparatus for pattern recognition employing the hidden Markov model

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.sub.m and y, (C.sub.m,y) into a signal u(y,m).epsilon.U=[a,b], where m=1, . . . , M, a,b .epsilon.R.sup.1 for 0.ltoreq.a.ltoreq.b, C={C.sub.1, C.sub.2, . . . , C.sub.M } is a set of signals against which the observation vector signal y is compared, y.epsilon.R.sup.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.sub.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.

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.sub.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.sub.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.sub.m is said membership value of y for F.sub.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.

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.sub.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.sub.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.sub.m is said membership value of y for F.sub.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.

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.sub.1, . . . , D.sub.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.sub.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.

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.sub.1, . . . , D.sub.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.sub.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.

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.sub.m in the conditions of predetermined S.sub.n, for a first set S={S.sub.1, S.sub.2, . . . ,S.sub.N } composed of N signals, and a second set C={C.sub.1, C.sub.2, . . . , C.sub.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.sub.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.sub.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.

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.sub.m and y, (C.sub.m,y) into a signal u(y,m).epsilon.U=[a,b], where m=1, . . . , M, a,b .epsilon.R.sup.1 for 0.ltoreq.a.ltoreq.b, C={C.sub.1, C.sub.2, . . . , C.sub.M } is a set of signals against which the observation vector signal y is compared, y.epsilon.R.sup.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.sub.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.