Continuous reference adaptation in a pattern recognition system
First Claim
1. An apparatus for pattern recognition of data input comprising:
- means for representing said data input as a set of observed vectors, wherein individual observed vectors of said set of observed vectors represent said data input at a different point in time;
means for comparing a first subset of said set of observed vectors to a set of models by comparing a set of reference vectors associated with said set of models to said set of observed vectors and identifying a resultant model which most closely matches said first subset, wherein said resultant model is one of said set of models;
means for creating a set of accumulation vectors wherein individual accumulation vectors of said set of accumulation vectors correspond to individual reference vectors of said set of reference vectors, and wherein a first accumulation vector of said set of accumulation vectors stores a first observed vector, and wherein said first observed vector was previously associated with a first reference vector of said set of reference vectors;
means for updating said set of reference vectors to create an updated set of reference vectors associated with said set of models to more accurately represent said data input, wherein said means for updating combines said first accumulation vector with said first reference vector; and
means for utilizing said updated set of reference vectors in comparing subsequent data input streams to said set of models.
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Abstract
A pattern recognition system which continuously adapts reference patterns to more effectively recognize input data from a given source. The input data is converted to a set or series of observed vectors and is compared to a set of Markov Models. The closest matching Model is determined and is recognized as being the input data. Reference vectors which are associated with the selected Model are compared to the observed vectors and updated ("adapted") to better represent or match the observed vectors. This updating method retains the value of these observed vectors in a set of accumulation vectors in order to base future adaptations on a broader data set. When updating, the system also may factor in the values corresponding to neighboring reference vectors that are acoustically similar if the data set from the single reference vector is insufficient for an accurate calculation. Every reference vector is updated after every input; thus reference vectors neighboring an updated reference vector may also be updated. The updated reference vectors are then stored by the computer system for use in recognizing subsequent inputs.
84 Citations
41 Claims
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1. An apparatus for pattern recognition of data input comprising:
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means for representing said data input as a set of observed vectors, wherein individual observed vectors of said set of observed vectors represent said data input at a different point in time; means for comparing a first subset of said set of observed vectors to a set of models by comparing a set of reference vectors associated with said set of models to said set of observed vectors and identifying a resultant model which most closely matches said first subset, wherein said resultant model is one of said set of models; means for creating a set of accumulation vectors wherein individual accumulation vectors of said set of accumulation vectors correspond to individual reference vectors of said set of reference vectors, and wherein a first accumulation vector of said set of accumulation vectors stores a first observed vector, and wherein said first observed vector was previously associated with a first reference vector of said set of reference vectors; means for updating said set of reference vectors to create an updated set of reference vectors associated with said set of models to more accurately represent said data input, wherein said means for updating combines said first accumulation vector with said first reference vector; and means for utilizing said updated set of reference vectors in comparing subsequent data input streams to said set of models. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computerized method for pattern recognition of data input, wherein said data input represents one or more unknown patterns, said method comprising the computer-implemented steps of:
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a) transforming said data input into a set of observed vectors, wherein individual observed vectors of said set of observed vectors represent said data input at a different point in time; b) comparing a first subset of said set of observed vectors to a set of models utilizing a set of reference vectors associated with said set of models and identifying a resultant model which most closely matches said first subset, wherein said resultant model is one of said set of models; c) creating a set of accumulation vectors wherein individual accumulation vectors of said set of accumulation vectors correspond to individual reference vectors of said set of reference vectors, and wherein a first accumulation vector of said set of accumulation vectors stores a first observed vector, and wherein said first observed vector was previously associated with a first reference vector of said set of reference vectors; d) updating said set of reference vectors to create a set of updated reference vectors associated with said set of models to more accurately represent said data input, wherein said updating includes combining said first accumulation vector with said first reference vector; and e) utilizing said set of updated reference vectors in comparing subsequent data input streams to said set of models. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. In a computer system, an apparatus for speech recognition of data input utilizing Markov Models comprising:
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an input device; a memory unit, said memory unit storing a set of Markov Models and a set of reference vectors associated with said set of Markov Models; a digital processor, said digital processor being coupled to said input device and said memory unit, said digital processor converting said data input to a set of observed vectors wherein individual observed vectors represent said data input at a different point in time, said digital processor comparing a first subset of said set of observed vectors to said set of Markov Models utilizing said set of reference vectors and identifying a resultant Markov Model of said set of Markov Models which most closely resembles said data input, said digital processor comparing said set of observed vectors to said set of reference vectors associated with said resultant Markov Model, said digital processor creating a set of accumulation vectors wherein individual accumulation vectors of said set of accumulation vectors respond to individual reference vectors of said set of reference vectors, and wherein a first accumulation vector of said set of accumulation vectors stores a first observed vector, and wherein said first observed vector was previously associated with a first reference vector of said set of reference vectors, said digital processor updating said set of reference vectors to create an updated set of reference vectors associated with said set of Markov Models to more accurately represent said observed vectors by combining said first accumulation vector with the first reference vector, said memory unit storing said updated set of reference vectors, and said digital processor utilizing said updated set of reference vectors in comparing subsequent input data to said set of Markov Models. - View Dependent Claims (26, 27, 28, 29)
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30. In a computer system, an apparatus for speech recognition of data input utilizing Markov Models comprising:
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an input device; a memory unit, said memory unit storing a set of Markov Models and a set of reference vectors associated with said set of Markov Models; a digital processor, said digital processor being coupled to said input device and said memory unit, said digital processor converting said data input to a set of observed vectors wherein individual observed vectors represent said data input at a different point in time, said digital processor comparing a first subset of said set of observed vectors to said set of Markov Models utilizing said set of reference vectors and identifying a resultant Markov Model of said set of Markov Models which most closely resembles said data input, said digital processor comparing said set of observed vectors to said set of reference vectors associated with said resultant Markov Model, said digital processor updating said set of reference vectors to create an updated set of reference vectors associated with said set of Markov Models to more accurately represent said set of observed vectors by combining a first observed vector or said first subset of said set of observed vectors with a first reference vector of said set of reference vectors, said memory unit storing said updated set of reference vectors, said digital processor utilizing said updated set of reference vectors in comparing subsequent input data to said set of Markov Models, said digital processor creating a set of accumulation vectors wherein individual accumulation vectors correspond to individual reference vectors in said set of reference vectors, said memory unit storing said set of accumulation vectors, said digital processor creating a set of counters wherein individual counters of said set of counters correspond to individual reference vectors of said set of reference vectors, each of said individual counters indicating the number of observed vectors which have been added to the accumulation vector of the corresponding reference vector, said memory unit storing said set of counters, said digital processor comparing each of said first subset of observed vectors to said set of reference vectors, said digital processor determining a closest reference vector to each of said first subset of observed vectors wherein said closest reference vector is more similar to said observed vector than any other reference vector in said set of reference vectors, said digital processor incrementing the counter corresponding to said closest reference vector, and said digital processor adding said observed vector to the accumulation vector corresponding to said closest reference vector. - View Dependent Claims (31, 32)
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33. A computer system for pattern recognition of data input utilizing Markov Models comprising:
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a bus; a memory unit coupled to said bus; a storage unit coupled to said bus which contains a set of models and a set of reference vectors associated with said set of models; a data input unit coupled to said bus which receives said data input; a signal processor which represents said data input as a set of observed vectors, wherein individual observed vectors of said set of observed vectors represent said data input at a different point in time; and a central processing unit (CPU) coupled to said bus which compares a first subset of said set of observed vectors to said set of models utilizing said set of reference vectors and identifies a resultant model which most closely matches said first subset, said resultant model being one of said set of models, wherein said CPU further creates an updated set of reference vectors associated with said set of models to more accurately represent said data input by combining a first observed vector of said first subset of said set of observed vectors with a first reference vector of said set of reference vectors, and utilizes said updated set of reference vectors in comparing subsequent data input streams to said set of models, and wherein said CPU includes, means for creating a set of accumulation vectors wherein individual accumulation vectors of said set of accumulation vectors correspond to individual reference vectors of said set of reference vectors, means for creating a set of counters such that individual counters of said set of counters correspond to individual reference vectors of said set of reference vectors, means for comparing each of said first subset of observed vectors to said set of reference vectors, means for determining a closest reference vector to each of said first subset of observed vectors wherein said closest reference vector is more similar to said observed vector than any other reference vector in said set of reference vectors, means for incrementing an individual counter corresponding to said closest reference vector, said individual counter indicating the number of observed vectors which have been added to the accumulation vector corresponding to said closest reference vector, and means for adding said observed vector to an accumulation vector corresponding to said closest reference vector. - View Dependent Claims (34, 35)
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36. A computer system for pattern recognition of data input utilizing Markov Models comprising:
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a bus; a memory unit coupled to said bus; a storage unit coupled to said bus which contains a set of models and a set of reference vectors associated with said set of models; a data input unit coupled to said bus which receives said data input; a signal processor which represents said data input as a set of observed vectors, wherein individual observed vectors of said set of observed vectors represent said data input at a different point in time; and a central processing unit (CPU) coupled to said bus which compares a first subset of said set of observed vectors to said set of models utilizing said set of reference vectors and identifies a resultant model which most closely matches said first subset, said resultant model being one of said set of models, wherein said CPU further creates a set of accumulation vectors wherein individual accumulation vectors of said set of accumulation vectors correspond to individual reference vectors of said set of reference vectors, wherein a first accumulation vector of said set of accumulation vectors stores a first observed vector, and wherein said first observed vector was previously associated with a first reference vector of said set of reference vectors, and wherein said CPU further creates an updated set of reference vectors associated with said set of models to more accurately represent said data input by combining said first accumulation vector with said first reference vector, and utilizes said updated set of reference vectors in comparing subsequent data input streams to said set of models. - View Dependent Claims (37, 38, 39, 40, 41)
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Specification