Pattern recognition using an observable operator model
First Claim
1. A method of pattern recognition, comprising:
- training a plurality of observable operator models for a plurality of characteristic events, the observable operator models comprising a plurality of observable operators;
receiving an unknown input;
computing a plurality of matching probabilities, one matching probability for each one of the plurality of characteristic events using the plurality of observable operators, wherein each matching probability is a probability that the unknown input matches a particular characteristic event;
selecting a maximum matching probability from the plurality of matching probabilities; and
displaying a characteristic event having the maximum matching probability.
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Abstract
Data structures, systems, and methods are aspects of pattern recognition using observable operator models (OOMs). OOMs are more efficient than Hidden Markov Models (HMMs). A data structure for an OOM has characteristic events, an initial distribution vector, a probability transition matrix, an occurrence count matrix, and at least one observable operator. System applications include computer systems, cellular phones, wearable computers, home control systems, fire safety or security systems, PDAs, and flight systems. A method of pattern recognition comprises training OOMs, receiving unknown input, computing matching probabilities, selecting the maximum probability, and displaying the match. A method of speech recognition comprises sampling a first input stream, performing a spectral analysis, clustering, training OOMs, and recognizing speech using the OOMs.
43 Citations
41 Claims
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1. A method of pattern recognition, comprising:
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training a plurality of observable operator models for a plurality of characteristic events, the observable operator models comprising a plurality of observable operators;
receiving an unknown input;
computing a plurality of matching probabilities, one matching probability for each one of the plurality of characteristic events using the plurality of observable operators, wherein each matching probability is a probability that the unknown input matches a particular characteristic event;
selecting a maximum matching probability from the plurality of matching probabilities; and
displaying a characteristic event having the maximum matching probability. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-readable medium having computer-executable instructions for performing a method of recognizing speech, the method comprising:
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sampling a first input stream, resulting in a plurality of samples;
performing a spectral analysis of the samples to obtain a plurality of feature vectors;
clustering the feature vectors to form a plurality of observation vectors;
training at least one observable operator model using the observation vectors; and
recognizing at least one part of speech from a second input stream using the at least one observable operator model. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A data structure of an observable operator model used to recognize patterns, comprising:
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a plurality of characteristic events corresponding to a input stream, the input stream comprising a plurality of stream elements and the input stream comprising a plurality of sequences;
an initial distribution vector, wherein each element of the initial distribution vector comprises a particular probability that a particular characteristic event is an initial event, the particular characteristic event being one of the plurality of characteristic events;
a probability transition matrix, wherein each element of the probability transition matrix comprises an estimate of a particular probability of producing a particular characteristic event, after observing a particular sequence;
an occurrence count matrix, wherein each element of the occurrence count matrix comprises an estimate of a particular probability of producing the particular characteristic event, after observing a particular stream element followed by the particular sequence; and
at least one observable operator calculable from the probability transition matrix and the occurrence count matrix;
wherein the plurality of characteristic events, the initial distribution vector, the probability transition matrix, the occurrence count matrix, and the at least one observable operator are storable on a storage medium during a training phase and retrievable during a recognition phase. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A method for recognizing speech, comprising:
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sampling an input signal;
converting the input signal to a discrete signal;
storing the discrete signal in a buffer;
reading a frame of data from the buffer;
checking for silence or noise in the frame;
removing any silence and noise from the frame;
spectrally flattening a signal in the frame;
performing frame windowing on the frame;
computing a moving weighted average for the frame;
performing feature extraction on the frame using a mathematical model;
clustering the frame with previously read frames;
training a plurality of observable operator models;
recognizing at least one unknown word using the observable operator models; and
displaying a recognized word corresponding to the at least one unknown word. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41)
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Specification