Method and system for pattern recognition based on dynamically constructing a subset of reference vectors
First Claim
1. A method for recognising a time-sequential input pattern which is derived from a continual physical quantity, said method comprising the steps of:
- accessing said physical quantity and therefrom generating a plurality of input observation vectors, representing said input pattern;
locating among a plurality of reference patterns a recognised reference pattern, which corresponds to said input pattern;
at least one reference pattern being a sequence of reference units, each reference unit being represented by at least one associated reference vector μ
a in a set {μ
a } of reference vectors, and said locating comprising selecting for each input observation vector o a subset {μ
s } of reference vectors from said set {μ
a } and calculating vector similarity scores between said input observation vector o and each reference vector μ
s of said subset {μ
s }, such that selecting a subset {μ
s } of reference vectors for each input observation vector o comprises calculating a measure of dissimilarity between said input observation vector o and each reference vector of said set {μ
a } and using as said subset {μ
s } of reference vectors a number of reference vectors μ
a, whose measures of dissimilarity with said input observation vector o are the smallest;
quantising each reference vector μ
a to a quantised reference vector R(μ
a), andwherein selecting the subset {μ
s } of reference vectors comprises, for each input observation vector o, the steps of;
quantising said input observation vector o to a quantised observation vector R(o),calculating for said quantised observation vector R(o) distances d(R(o), R(μ
a)) to each quantised reference vector R(μ
a), andusing said distance d(R(o), R(μ
a)) as said measure of dissimilarity between said input observation vector o and said reference vector μ
a ; and
quantising a vector x which is one of a reference vector μ
a or an observation vector o to a quantised vector R(x) comprising calculating a sign vector S(x) by assigning to each component of said sign vector a binary value, with a first binary value b1 being assigned if the corresponding component of the vector x has a negative value and a second binary value b2 being assigned if the corresponding component of the vector x has a positive value.
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Abstract
A system and method are used for recognising a time-sequential input pattern (20), which is derived from a continual physical quantity, such as speech. The system has input means (30), which accesses the physical quantity and therefrom generates a plurality of input observation vectors. The input observation vectors represent the input pattern. A reference pattern database (40) is used for storing a plurality of reference patterns. Each reference pattern includes a sequence of reference units, where each reference unit is represented by at least one associated reference vector μa in a set {μa } of reference vectors. A localizer (50) is used for locating among the reference patterns stored in the reference pattern database (40), a recognised reference pattern, which corresponds to the input pattern. The locating includes selecting a subset {μs } of reference vectors from said set {μa } for each input observation vector o by calculating a measure of dissimilarity between the input observation vector o and each reference vector of the set {μa }. A number of reference vectors μa, whose measures of dissimilarity with said input observation vector o are the smallest, are used as the subset {μs } of reference vectors. The reference vectors of the subset {μs } are used to calculate for each reference pattern, a pattern similarity score. The recognised pattern is one of the reference patterns for which an optimum of the pattern similarity scores is calculated. Output means (70) are used for outputting the recognised pattern.
205 Citations
19 Claims
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1. A method for recognising a time-sequential input pattern which is derived from a continual physical quantity, said method comprising the steps of:
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accessing said physical quantity and therefrom generating a plurality of input observation vectors, representing said input pattern; locating among a plurality of reference patterns a recognised reference pattern, which corresponds to said input pattern;
at least one reference pattern being a sequence of reference units, each reference unit being represented by at least one associated reference vector μ
a in a set {μ
a } of reference vectors, and said locating comprising selecting for each input observation vector o a subset {μ
s } of reference vectors from said set {μ
a } and calculating vector similarity scores between said input observation vector o and each reference vector μ
s of said subset {μ
s }, such that selecting a subset {μ
s } of reference vectors for each input observation vector o comprises calculating a measure of dissimilarity between said input observation vector o and each reference vector of said set {μ
a } and using as said subset {μ
s } of reference vectors a number of reference vectors μ
a, whose measures of dissimilarity with said input observation vector o are the smallest;quantising each reference vector μ
a to a quantised reference vector R(μ
a), andwherein selecting the subset {μ
s } of reference vectors comprises, for each input observation vector o, the steps of;quantising said input observation vector o to a quantised observation vector R(o), calculating for said quantised observation vector R(o) distances d(R(o), R(μ
a)) to each quantised reference vector R(μ
a), andusing said distance d(R(o), R(μ
a)) as said measure of dissimilarity between said input observation vector o and said reference vector μ
a ; andquantising a vector x which is one of a reference vector μ
a or an observation vector o to a quantised vector R(x) comprising calculating a sign vector S(x) by assigning to each component of said sign vector a binary value, with a first binary value b1 being assigned if the corresponding component of the vector x has a negative value and a second binary value b2 being assigned if the corresponding component of the vector x has a positive value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for recognising a time-sequential input pattern, which is derived from a continual physical quantity, said system comprising:
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input means for accessing said physical quantity and therefrom generating a plurality of input observation vectors, representing said input pattern; a reference pattern database for storing a plurality of reference patterns;
at least one reference pattern being a sequence of reference units, each reference unit being represented by at least one associated reference vector μ
a in a set {μ
a } of reference vectors;a localizer for locating among the reference patterns stored in said reference pattern database a recognised reference pattern, which corresponds to said input pattern, said locating comprising selecting for each input observation vector o a subset {μ
s } of reference vectors from said set {μ
a } and calculating vector similarity scores between said input observation vector o and each reference vector μ
s of said subset {μ
s }; andoutput means for outputting said recognised pattern;
wherein said selecting of a subset {μ
s } of reference vectors for each input observation vector o comprises calculating a measure of dissimilarity between said input observation vector o and each reference vector of said set {μ
a } and using as said subset {μ
s } of reference vectors a number of reference vectors μ
a, whose measures of dissimilarity with said input observation vector o are the smallest; andwherein said reference pattern database further stores for each reference vector μ
a a quantised reference vector R(μ
a), and selecting the subset {μ
s } of reference vectors comprises for each input observation vector o means for executing the steps of;quantising said input observation vector o to a quantised observation vector R(o), calculating for said quantised observation vector R(o) distances d(R(o), R(μ
a)) to each quantised reference vector R(μ
a), andusing said distance d(R(o), R(μ
a)) as said measure of dissimilarity between said input observation vector o and said reference vector μ
a ; andmeans for quantising a vector x which is one of a reference vector μ
a or an observation vector o as a quantised vector R(x) is proportional to a sign vector S(x), each component of said sign vector S(x) comprising a first binary value b1 if the corresponding component of the vector x has a negative value and a second binary value b2 if the corresponding component of the vector x has a positive value. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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Specification