Embedded coupled hidden markov model
First Claim
1. A method for generating an embedded couple hidden Markov model, comprising:
- obtaining data of two or more dimensions, the data having a label representing a known pattern;
segmenting the data uniformly into one or more super segments;
assigning each super segment to a super state of a super channel in a coupled hidden Markov model at a super layer of an embedded coupled hidden Markov model, the super layer having at least one super channel;
segmenting each super segment into lower layer segments, each of which corresponds to a lower layer state of a lower channel of a lower layer coupled hidden Markov model associated with one of the super states;
optimally segmenting the data at the lower layer to produce an optimal segmentation;
updating one or more parameters of at least one model associated with at least one lower layer state based on the optimal segmentation; and
updating one or more parameters of at least one model associated with at least one super state based on the at least one model associated with the at least one lower layer state to generate an embedded coupled hidden Markov model modeling the known pattern.
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Abstract
An arrangement is provided for embedded coupled hidden Markov model. To train an embedded coupled hidden Markov model, training data is first segmented into uniform segments at different layers of the embedded coupled hidden Markov model. At each layer, a uniform segment corresponds to a state of a coupled hidden Markov model at that layer. An optimal segmentation is generated at the lower layer based on the uniform segmentation and is then used to update parameters of models associated with the states of coupled hidden Markov models at lower layer. The updated model parameters at the lower layer are then used to update the model parameters associated with states at the super layer.
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Citations
29 Claims
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1. A method for generating an embedded couple hidden Markov model, comprising:
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obtaining data of two or more dimensions, the data having a label representing a known pattern;
segmenting the data uniformly into one or more super segments;
assigning each super segment to a super state of a super channel in a coupled hidden Markov model at a super layer of an embedded coupled hidden Markov model, the super layer having at least one super channel;
segmenting each super segment into lower layer segments, each of which corresponds to a lower layer state of a lower channel of a lower layer coupled hidden Markov model associated with one of the super states;
optimally segmenting the data at the lower layer to produce an optimal segmentation;
updating one or more parameters of at least one model associated with at least one lower layer state based on the optimal segmentation; and
updating one or more parameters of at least one model associated with at least one super state based on the at least one model associated with the at least one lower layer state to generate an embedded coupled hidden Markov model modeling the known pattern. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method, comprising:
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deriving at least one embedded coupled hidden Markov model based on one or more training samples, each of which having a label representing a known pattern, each of the at least one embedded coupled hidden Markov model modeling a corresponding known pattern;
receiving input data of two or more dimensions, the input data being indicative of a pattern;
recognizing the pattern in the input data with respect to the known patterns based on the at least one embedded coupled hidden Markov model. - View Dependent Claims (11, 12, 13)
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14. A system, comprising:
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at least one coupled hidden Markov model at a lower layer, each of the at least one coupled hidden Markov model having a plurality of channels, each of which having a plurality of lower layer states;
a coupled hidden Markov model at a super layer with a plurality of super channels, each of the super channels having a plurality of super states, each of which embeds one of the at least one coupled hidden Markov model at the lower layer. - View Dependent Claims (15, 16, 17)
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18. A system, comprising:
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an embedded coupled hidden Markov model training mechanism to train at least one embedded coupled hidden markov model using a plurality of training samples;
an embedded coupled hidden Markov model based recognition mechanism to recognize a pattern from input data, producing a recognition result. - View Dependent Claims (19, 20, 21)
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22. An article comprising a storage medium having stored thereon instructions that, when executed by a machine, result in the following:
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obtaining data of two or more dimensions, the data having a label representing a known pattern;
segmenting the data uniformly into one or more super segments;
assigning each super segment to a super state of a super channel in a coupled hidden Markov model at a super layer of an embedded coupled hidden Markov model, the super layer having at least one super channel;
segmenting each super segment into lower layer segments, each of which corresponds to a lower layer state of a lower channel of a lower layer coupled hidden Markov model associated with one of the super states;
optimally segmenting the data at the lower layer to produce an optimal segmentation;
updating one or more parameters of at least one model associated with at least one lower layer state based on the optimal segmentation; and
updating one or more parameters of at least one model associated with at least one super state based on the at least one model associated with the at least one lower layer state to generate an embedded coupled Markov model modeling the known pattern. - View Dependent Claims (23, 24, 25, 26)
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27. An article comprising a storage medium having stored thereon instructions that, when executed by a machine, result in the following:
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deriving at least one embedded coupled hidden Markov model, each of which modeling a known pattern, using one or more training samples, each of which having a label representing a known pattern, each of the at least one embedded hidden Markov model modeling a corresponding known pattern. receiving input data of two or more dimensions, the input data containing data indicative of a pattern;
recognizing the pattern from the input data with respect to the known patterns using the at least one embedded coupled hidden Markov model. - View Dependent Claims (28, 29)
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Specification