Pattern recognition apparatus and method for making same

Claim 2

A pattern recognition apparatus for identifying the category of an input pattern from various categories of value patterns stored in the apparatus comprising: a vector generating means for converting an input pattern signal to an input vector representing the characteristics of the input pattern; a dictionary means for storing a plurality of reference vectors for each of said various categories, including a first memory means for storing a plurality of predetermined first reference vectors for each of said categories, said first reference vectors representative of features common to said categories, and a second memory means for storing a plurality of subsequently determined second reference vectors for each of said categories, said second reference vectors representative of features particular to said categories, said second reference vectors being mutually exclusive of said first reference vectors; a reference vector generating means for generating said second reference vectors from the input vector and for storing them in said second memory means, said reference vector generating means generating said second reference vectors by subtracting from the input vector a vector having components corresponding to the angles between the input vector and each of said first reference vectors; a similarity calculating means for calculating the similarities between said input vector and reference vectors stored in said dictionary means for each of said categories; and a comparing means for comparing the similarities calculated for each of said categories and for identifying the category of the input pattern.

Claim 3

A pattern recognition apparatus of claim 1 wherein the similarity calculating means comprises: a scalar product calculating means for calculating the scalar product between the input vector and each of said reference vectors; squaring means for squaring each output of said scalar product calculating means; and adding means for adding each of said squared outputs.

Claim 4

A pattern recognition apparatus of claim 2 wherein said first and second memory means store coefficients corresponding to each of said reference vectors, said similarity calculating means further comprising a multiplying means for multiplying the output of said squaring means by the corresponding coefficient, and said reference vector generating means further comprising a coefficient calculating means for calculating the coefficient corresponding to the generated second reference vector from the coefficients of the first reference vectors.

Claim 5

A pattern recognition apparatus according to claim 1 or 2 in which said vector generating means includes an input means for converting said input pattern into electrical signals, and a pre-processing means, receiving said electrical signals, for extracting said input vector representing the characteristic of the input pattern from said electrical signals.

Claim 6

A pattern recognition apparatus according to claim 4 wherein said input pattern is a voice pattern, and said input means comprises a microphone.

Claim 7

A pattern recognition apparatus according to claim 4 wherein said pre-processing means comprises: a plurality of band-pass filters, each filter having a different frequency range; a plurality of squaring circuits, each coupled to a corresponding bandpass filter; and a plurality of low pass filters, each coupled to a corresponding squaring circuit.

Claim 8

A pattern recognition apparatus according to claim 2 wherein said similarity calculating means further comprises an absolute value squaring means for obtaining the squared absolute value of the input vector, and a dividing means for dividing the output of said adding means by the output of the absolute value squaring means.

Claim 9

A pattern recognition apparatus according to claim 1 or 2 wherein said comparing means comprises: a detecting means for determining the maximum value of said calculated similarities and the second largest value of said calculated similarities; a first comparing means for comparing said maximum value with a predetermined first threshold value; and, a second comparing means for comparing the difference between said maximum value and said second largest value with a predetermined second threshold value.

Claim 10

A pattern recognition apparatus according to claim 1 or 2 wherein said reference vector generating means includes a category designating means for designating the desired category for the generated second reference vectors and a reference vector calculating means for calculating and generating said second reference vectors for the category A according to the following equation: ##EQU16## wherein ψ x .sup.(A) is the second reference vectors to be generated, F is the input vector, φ m .sup.(A) (m=1, - - - ,M.sup.(A)) are the first reference vectors of category A wherein category A is designated by the designating means, M.sup.(A) is a number of first reference vectors of category A, ψ n .sup.(A) (n-1, - - - , N.sup.(A)) are the second reference vectors of category A previously stored in the second memory means, N.sup.(A) is a number of second reference vectors of category A previously stored, (F, φ m .sup.(A)) denotes the calculation of a scalar product between vector F and vector φ m .sup.(A).

Claim 11

A pattern recognition apparatus for identifying the category of an input pattern from various categories of reference patterns stored in the apparatus, comprising: a vector generating means for converting an input pattern signal to an input vector representing the characteristics of the input pattern; a dictionary means for storing a plurality of reference vectors for each of said various categories, including a first memory means for storing a plurality of predetermined first reference vectors for each of said categories, said first reference vectors representative of features common to said categories, and a second memory means for storing a plurality of subsequently determined second reference vectors for each of said categories, said second reference vectors representative of features particular to said categories, said second reference vectors being mutually exclusive of said first reference vectors; a reference vector generating means for generating said second reference vectors from the input vector and said first reference vectors and for storing them in said second memory means, said second reference vectors representing characteristics which are specific to said input pattern; a similarity calculating means for calculating the similarities between said input vector supplied from the vector generating means and reference vectors stored in said dictionary means for each of said categories, said similarity calculating means comprising a scalar product calculating means for calculating the scalar product between the input vector and each of said reference vectors, a squaring means for squaring each output of said scalar product calculating means, and an adding means for adding each of said squared outputs; and a comparing means for comparing the similarities calculated for each of said categories and for identifying the category of the input pattern.

Claim 12

A pattern recognition apparatus for identifying the category of an input pattern from various categories of reference patterns stored in the apparatus, comprising: a vector generating means for converting an input pattern signal to an input vector representing the characteristics of the input pattern; a dictionary means for storing a plurality of reference vectors for each of said various categories, including a first memory means for storing a plurality of predetermined first reference vectors for each of said categories, said first reference vectors representative of features common to said categories, and a second memory means for storing a plurality of subsequently determined second reference vectors for each of said categories, said second reference vectors representative of features particular to said categories, said second reference vectors being mutually exclusive of said first reference vectors; a reference vector generating means for generating said second reference vectors from the input vector and said first reference vectors and for storing them in said second memory means, said second reference vectors representing characteristics which are specific to said input pattern; a similarity calculating means for calculating the similarities between said input vector supplied from the vector generating means and reference vectors stored in said dictionary means for each of said categories, said similarity calculating means comprising a scalar product calculating means for calculating the scalar product between the input vector and each of said reference vectors, a squaring means for squaring each output of said scalar product calculating means, and an adding means for adding each of said squared outputs; and a comparing means for comparing the similarities calculated for each of said categories and for identifying the category of the input pattern.

Claim 13

In a pattern recognition system for identifying the category of an input pattern from various categories of reference patterns stored in the system including means for generating an input vector representing the characteristics of the input pattern to be identified, a dictionary means for storing sets of reference vectors each set representing the characteristics of a particular category of reference patterns, a similarity calculating means for determining the similarities for every category by calculating the squared scalar products between the input vector and each set of reference vectors, a comparing means for comparing the category to which the input pattern belongs by comparing said calculated similarities and a reference vector generating means for generating an additional reference vector which is added to the set of reference vectors previously stored, the system having a recognizing mode wherein an unknown input pattern is identified and a learning mode wherein the reference vector generating means is energized, the method of generating said additional reference vector during the learning mode comprising the steps of: supplying said input vector to the reference vector generating means; specifying the category to which the additional reference vector belongs; transferring the sets of reference vectors of the specified category from the dictionary means to the vector generating means; obtaining said additional reference vector by removing components of the transferred reference vectors from the supplied input vector; and storing the additional reference vector in the dictionary means as one of the reference vectors of the specified category.

Claim 14

In a pattern recognition system for identifying the category of an input pattern from various categories of reference patterns stored in the system including means for generating an input vector representing the characteristics of the input pattern to be identified, a dictionary means for storing sets of reference vectors each set representing the characteristics of a particular category of reference patterns, a similarity calculating means for determining the similarities for every category by calculating the squared scalar products between the input vector and each set of reference vectors, a comparing means for comparing the category to which the input pattern belongs by comparing said calculated similarities and a reference vector generating means for generating an additional reference vector which is added to the set of reference vectors previously stored, the system having a recognizing mode wherein an unknown input pattern is identified and a learning mode wherein the reference vector generating means is energized, the method of generating said additional reference vector during the learning mode comprising the steps of: supplying said input vector to the reference vector generating means; specifying the category to which the additional reference vector belongs; transferring the sets of reference vectors of the specified category from the dictionary means to the vector generating means; obtaining said additional reference vector by removing components of the transferred reference vectors from the supplied input vector; and storing the additional reference vector in the dictionary means as one of the reference vectors of the specified category.