Methods and apparatus for fast and robust model training for object classification
First Claim
1. A data object classification system, comprising:
- a classification module for receiving data objects, each data object comprising a set of observations and identifying a class to which each data object belongs, the classification module using a set of models to process the data objects, each model representing one class to which a data object may belong; and
a training module for optimizing parameters to be used in the models employed by the classification module, the training module receiving a set of training data as an input and processing the training data to create initial estimates of the parameters for the models, the training module being further operative to update the parameters by computing closed form solutions for the parameters, the closed form solutions for each model being chosen to maximize the aggregate a posteriori probability that the model will correctly assign a data object to the class associated with the model.
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Abstract
Techniques for fast and robust data object classifier training are described. A process of classifier training creates a set of Gaussian mixture models, one model for each class to which data objects are to be assigned. Initial estimates of model parameters are made using training data. The model parameters are then optimized to maximize an aggregate a posteriori probability that data objects in the set of training data will be correctly classified. Optimization of parameters for each model is performed through the process of a number of iterations in which the closed form solutions are computed for the model parameters of each model, the model performance is tested to determine if the newly computed parameters improve the model performance and the model is updated with the newly computed parameters if performance has improved. At each new iteration, the parameters computed in the previous iteration are used as initial estimates.
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Citations
17 Claims
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1. A data object classification system, comprising:
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a classification module for receiving data objects, each data object comprising a set of observations and identifying a class to which each data object belongs, the classification module using a set of models to process the data objects, each model representing one class to which a data object may belong; and
a training module for optimizing parameters to be used in the models employed by the classification module, the training module receiving a set of training data as an input and processing the training data to create initial estimates of the parameters for the models, the training module being further operative to update the parameters by computing closed form solutions for the parameters, the closed form solutions for each model being chosen to maximize the aggregate a posteriori probability that the model will correctly assign a data object to the class associated with the model. - View Dependent Claims (2, 3, 4, 5, 6, 14, 15, 16, 17)
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7. A process of classifier training for training the classifier to correctly identify each class of a plurality of classes to which each data object in a set of training data belongs, each data object comprising a set of observations providing representations of characteristics of the object useful for classifying the object, comprising the steps of:
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receiving and analyzing a set of training data comprising a set of data objects, each data object in the training data comprising set of observations and a label identifying the class to which the data object belongs implementing a set of models, each model to be optimized to correctly classify the set of training data, one model representing each of the plurality of classes;
simultaneously estimating initial parameters for all models for the parameters to be optimized;
for each model, computing closed form solutions for the parameters to be optimized, the solutions being computed in order to maximize the aggregate a posteriori probability that the model will correctly assign a data object to the class associated with the model. - View Dependent Claims (8, 9, 10, 11)
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12. A speaker identification system, comprising:
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a data extractor for receiving speech signals and extracting identifying characteristics of the speech signals to create data objects comprising characteristics of the speech signals useful for identifying a speaker producing the speech;
a speaker identification module for receiving one or more data objects and identifying the speaker producing the speech signal associated with the data object, the speaker identification module implementing a set of models to process the speech signals, each model being associated with a possible speaker; and
a training module for optimizing parameters to be used in the models implemented by the speaker identification module, the training module receiving a set of training data as an input and processing the training data to create initial estimates of the parameters for the models, the training module being further operative to update the parameters by computing closed form solutions for the parameters, the closed form solutions for each model being chosen to maximize the aggregate a posteriori probability that the model will correctly associate a data object with the speaker producing the speech signal from which the data object was created. - View Dependent Claims (13)
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Specification