Method and system for pattern recognition based on tree organized probability densities
First Claim
1. A method for recognising an input pattern which is derived from a continual physical quantity, said method comprising the steps of:
- accessing said physical quantity and therefrom generating a sequence 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 probability density in a set of reference probability densities;
representing a selection of the reference probability densities as a tree structure, where each leaf node corresponds to a reference probability density, and where each non-leaf node corresponds to a cluster probability density, which is derived from reference probability densities corresponding to leaf nodes in branches of said non-leaf node;
said locating comprising for each input observation vector o;
selecting a plurality of leaf nodes by searching said tree structure via non-leaf nodes for which the corresponding cluster probability density gives an optimum cluster likelihood for said input observation vector o; and
calculating an observation likelihood of said input observation vector o for each reference probability density which corresponds to a selected leaf node,said method comprising representing the reference probability densities associated with each reference unit as a separate tree structure, andsaid locating comprising selecting leaf nodes of each separate tree structure by performing said searching for each separate tree structure.
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Accused Products
Abstract
A time-sequential input pattern (20), which is derived from a continual physical quantity, such as speech is recognized. The system includes input means (30), which accesses the physical quantity and therefrom generates a sequence of input observation vectors. The input observation vectors represent the input pattern. A reference pattern database (40) is used for storing reference patterns, which consist of a sequence of reference units. Each reference unit is represented by associated reference probability densities. A tree builder (60) represents for each reference unit the set of associated reference probability densities as a tree structure. Each leaf node of the tree corresponds to a reference probability density. Each non-leaf node corresponds to a cluster probability density, which is derived from all reference probability densities corresponding to leaf nodes in branches below the non-leaf node. 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, for each input observation vector, searching each tree structure for reference probability densities which give a high likelihood for the observation vector. Each tree is searched by selecting at the level immediately below the root node a number of nodes for which the corresponding cluster probability densities give an optimum cluster likelihood. This is repeated at successively lower levels of the tree by using each selected node as a root node, until the selected node is a leaf node. For each selected leaf node, the corresponding reference probability density is used to calculate the likelihood of the input observation vector. These likelihoods are combined per reference pattern to give 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.
52 Citations
10 Claims
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1. A method for recognising an 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 sequence 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 probability density in a set of reference probability densities;representing a selection of the reference probability densities as a tree structure, where each leaf node corresponds to a reference probability density, and where each non-leaf node corresponds to a cluster probability density, which is derived from reference probability densities corresponding to leaf nodes in branches of said non-leaf node; said locating comprising for each input observation vector o; selecting a plurality of leaf nodes by searching said tree structure via non-leaf nodes for which the corresponding cluster probability density gives an optimum cluster likelihood for said input observation vector o; and calculating an observation likelihood of said input observation vector o for each reference probability density which corresponds to a selected leaf node, said method comprising representing the reference probability densities associated with each reference unit as a separate tree structure, and said locating comprising selecting leaf nodes of each separate tree structure by performing said searching for each separate tree structure. - View Dependent Claims (2, 3, 4, 5)
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6. 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 sequence of input observation vectors, representing said input pattern; a tree builder for representing a selection of reference probability densities from a set of reference probabilities as a tree structure, where each leaf node corresponds to a reference probability density, and where each non-leaf node corresponds to a cluster probability density, which is derived from all reference probability densities corresponding to leaf nodes in branches below said non-leaf node; 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 probability density in said set of reference probability densities, said selection of reference probability densities being stored as said tree structure;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 for each input observation vector o; selecting a plurality of leaf nodes by searching said tree structure via non-leaf nodes for which the corresponding cluster probability density gives an optimum cluster likelihood for said input observation vector o, and calculating an observation likelihood of said input observation vector o for each reference probability density which corresponds to a selected leaf node; and output means for outputting said recognised pattern;
wherein;said tree builder is conceived to for each reference unit represent the associated reference probability densities as a separate tree structure, said reference pattern database further stores for each reference unit said separate tree structure, and said locating comprises selecting leaf nodes of each separate tree structure by performing said searching for each separate tree structure. - View Dependent Claims (7, 8, 9, 10)
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Specification