Contents 1. INTRODUCTION
The EB procedure involves two types of transformations that can be described as follows. Let be some function in variables and .
Abstract:
The discrimination technique for estimating the parameters of Gaussian mixtures that is based on the extended Baum transformations (EB) has had significant impact on the ...Show MoreMetadata
Abstract:
The discrimination technique for estimating the parameters of Gaussian mixtures that is based on the extended Baum transformations (EB) has had significant impact on the speech recognition community. There appear to be no published proofs that definitively show that these transformations increase the value of an objective function with iteration (i.e., so-called "growth transformations"). The proof presented in the current paper is based on the linearization process and the explicit growth estimate for linear forms of Gaussian mixtures. We also derive new transformation formulae for estimating the parameters of Gaussian mixtures generalizing the EB algorithm, and run simulation experiments comparing different growth transformations.
Date of Conference: 17-21 May 2004
Date Added to IEEE Xplore: 30 August 2004
Print ISBN:0-7803-8484-9
Print ISSN: 1520-6149
Citations are not available for this document.
Cites in Patents (2)Patent Links Provided by 1790 Analytics
1.
Kanevsky, Dimitri; Nahamoo, David; Ramabhadran, Bhuvana; Sainath, Tara N., "Directional optimization via EBW"
Inventors:
Kanevsky, Dimitri; Nahamoo, David; Ramabhadran, Bhuvana; Sainath, Tara N.
Abstract:
An optimization system and method includes determining a best gradient as a sparse direction in a function having a plurality of parameters. The sparse direction includes a direction that maximizes change of the function. This maximum change of the function is determined by performing an optimization process that gives maximum growth subject to a sparsity regularized constraint. An extended Baum Welch (EBW) method can be used to identify the sparse direction. A best step size is determined along the sparse direction by finding magnitudes of entries of direction that maximizes the function restricted to the sparse direction. A solution is recursively refined for the function optimization using a processor and storage media.
Assignee:
INTERNATIONAL BUSINESS MACHINES CORP
Filing Date:
11 May 2010
Grant Date:
03 September 2013
Patent Classes:
Current U.S. Class:
708207000
Current International Class:
G06F0070000000
2.
Kanevsky, Dimitri; Nahamoo, David; Povey, Daniel; Ramabhadran, Bhuvana, "SYSTEM AND METHOD FOR OPTIMIZING PATTERN RECOGNITION OF NON GAUSSIAN PARAMETERS"
Inventors:
Kanevsky, Dimitri; Nahamoo, David; Povey, Daniel; Ramabhadran, Bhuvana
Abstract:
A method of optimizing a function of a parameter includes associating, with an objective function for initial value of parameters, an auxiliary function of parameters that could be optimized computationally more efficiently than an original objective function, obtaining parameters that are optimum for the auxiliary function, obtaining updated parameters by taking a weighted sum of the optimum of the auxiliary function and initial model parameters.
Assignee:
INTERNATIONAL BUSINESS MACHINES CORP
Filing Date:
02 April 2008
Grant Date:
22 May 2012
Patent Classes:
Current U.S. Class:
706012000
Current International Class:
G06F0151800
