Apparatus and method for normalizing and categorizing linear prediction code vectors using Bayesian categorization technique
First Claim
1. A pattern matching system provided for performing a sequence of single syllables recognition comprising:
- a dictionary means for storing a plurality of standard patterns wherein each of said standard patterns representing a single standard syllable by a set of feature vectors C(1), C(2), C(3), . . . , and C(M) and M being a positive integer;
a converting means for converting an input pattern representing single unknown syllable into a categorizing pattern for representing said single unknown syllable in a set of categorizing vectors X where X={x(1), x(2),x(3), . . . ,x(k)} where k representing a positive integer; and
a Bayesian-decision-rule categorizing means for computing a conditional normal density function ƒ
(x| Ci) for each of said feature vectors Ci, wherein said function ƒ
(x| Ci) having a normal distribution and said x(1), x(2), x(3), . . . and x(k) are stochastically independent; and
said Bayesian-decision-rule categorizing means further employing functional parameters of said normal distribution for said normal density function ƒ
(x| Ci) to apply a Bayesian decision rule to deterministically identify said single unknown syllable with one of said standard single syllables.
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Abstract
The present invention discloses a pattern matching system applicable for syllable recognition which includes a dictionary means for storing a plurality of standard patterns each representing a standard syllable by at least a syllable feature. The pattern matching system further includes a converting means for converting an input pattern representing an unknown syllable into a categorizing pattern for representing the unknown syllable in the syllable features used for representing the standard syllables. The pattern matching system further includes a Bayesian categorizing means for matching the standard pattern representing the standard syllable and the categorizing pattern representing the unknown syllable for computing a Bayesian mis-categorization risk for each of the standard syllables, the Bayesian categorization means further including a comparing and identification means for selecting a standard syllable which has the least mis-categorization risk as an identified syllable for the input unknown syllable.
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Citations
14 Claims
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1. A pattern matching system provided for performing a sequence of single syllables recognition comprising:
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a dictionary means for storing a plurality of standard patterns wherein each of said standard patterns representing a single standard syllable by a set of feature vectors C(1), C(2), C(3), . . . , and C(M) and M being a positive integer; a converting means for converting an input pattern representing single unknown syllable into a categorizing pattern for representing said single unknown syllable in a set of categorizing vectors X where X={x(1), x(2),x(3), . . . ,x(k)} where k representing a positive integer; and a Bayesian-decision-rule categorizing means for computing a conditional normal density function ƒ
(x| Ci) for each of said feature vectors Ci, wherein said function ƒ
(x| Ci) having a normal distribution and said x(1), x(2), x(3), . . . and x(k) are stochastically independent; andsaid Bayesian-decision-rule categorizing means further employing functional parameters of said normal distribution for said normal density function ƒ
(x| Ci) to apply a Bayesian decision rule to deterministically identify said single unknown syllable with one of said standard single syllables. - View Dependent Claims (2, 3, 4, 5)
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6. A pattern matching system provided for performing a sequence of single syllables recognition comprising:
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a receiver for receiving an incoming syllable utterance of either a standard syllable or a single unknown syllable for syllable recognition in the form of wavefunctions; an analog to digital conversion means for converting said wavefunctions to a plurality of digital data representing said wave functions; a linear predictive coding (LPC) means for converting said digital data representing said input wavefunction into a LPC cepstra vector; a compression means for compressing said each LPC cepstra vector into a compressed cepstra vector wherein said unknown syllable is represented by a set of categorizing vectors X where X={x(1),x(2),x(3), . . . ,x(k)}; a dictionary means for storing a plurality of standard compressed cepstra vectors each representing a standard single syllable by a set of feature vectors C(1), C(2), C(3), . . . , and C(M); a Bayesian-decision-rule categorizing means for computing a conditional normal density function ƒ
(x| Ci) for each of said feature vectors Ci, assuming that said function ƒ
(x| Ci) having a normal distribution and said x(1), x(2), x(3), . . . and x(k) are stochastically independent;
,said Bayesian-decision-rule categorizing means further employing functional parameters of said normal distribution for said normal density function ƒ
(x| Ci) to apply a Bayesian decision rule to deterministically identify said single unknown syllable with one of said standard syllables; andan user interface means to low an user of said pattern matching system to provide input data and commands for controlling the operation of said matching system. - View Dependent Claims (7, 9)
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8. The pattern matching system of daim 7 wherein:
said compression means compressing said LPC cepstra vectors represented by Y'"'"'k where Y'"'"'k={y'"'"'(k)1, y'"'"'(k)2, y'"'"'(k)3, . . . , y'"'"'(k)p } and K=1,2,3 . . . , m, according to a sum S1 of absolute differences of two consecutive LPC cepstra vectors wherein;
##EQU13## and said LPC cepstra vectors are divided into a plurality sections with an average value of said LPC cepstra vectors in each of section characterized by a sum-difference-feature of said section.
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10. A method for matching and categorizing an input pattern of waveform applicable for syllable recognition comprising the steps of:
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(a) storing in a dictionary means a plurality of standard patterns wherein each of said standard patterns representing a standard single syllable by a set of feature vectors C(1), C(2), C(3), . . . , and C(M) and M being a positive integer; (b) converting said input pattern of waveform representing an unknown single syllable into a categorizing pattern for representing said unknown syllable by a set of categorizing vectors X where X={x(1), x(2), x(3), . . . ,x(k)} and k being a positive integer; (c) utilizing a Bayesian-decision-rule categorizing means for computing a conditional normal density function ƒ
(x| Ci) wherein said function ƒ
(x| Ci) having a normal distribution and said x(1), x(2), x(3), . . . and x(k) are stochastically independent;
, and(d) employing functional parameters of said normal distribution for said normal density function ƒ
(x| Ci) to apply a Bayesian decision rule to identify said input unknown single syllable with one of said standard single syllables. - View Dependent Claims (11, 12, 13, 14)
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Specification