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AssigneeInternational Business Machines Corporation
InventorsRamesh Ambat Gopinath, Mukund Padmanabhan, George Andrei Saon
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The present invention provides a new approach to heteroscedastic linear discriminant analysis (HDA) by defining an objective function which maximizes the class discrimination in the projected subspace while ignoring the rejected dimensions. Moreover, we present a link between discrimination and the likelihood of the projected samples and show that HDA can be viewed as a constrained maximum likelihood (ML) projection for a full covariance gaussian model, the constraint being given by the maximization of the projected between-class scatter volume. The present invention also provides that, under diagonal covariance gaussian modeling constraints, applying a diagonalizing linear transformation (e.g., MLLT—maximum likelihood linear transformation) to the HDA space results in an increased classification accuracy. In another embodiment, the heteroscedastic discriminant objective function assumes that models associated with the function have diagonal covariances thereby resulting in a diagonal heteroscedastic discriminant objective function.
A method for use in a pattern recognition system of processing feature vectors extracted from a pattern signal input to the system, the method comprising the steps of:
Apparatus for use in a pattern recognition system of processing feature vectors extracted from a pattern signal input to the system, the apparatus comprising:
Apparatus for use in a speech recognition system of processing feature vectors extracted from a speech signal input to the system, the apparatus comprising:
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