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Maximum Likelihood Linear Transformations for HMMBased Speech Recognition
 COMPUTER SPEECH AND LANGUAGE
, 1998
"... This paper examines the application of linear transformations for speaker and environmental adaptation in an HMMbased speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias ..."
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Cited by 570 (68 self)
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This paper examines the application of linear transformations for speaker and environmental adaptation in an HMMbased speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple
Linear Transforms
"... Very important class of functions: signal processing, scientific computing, … Mathematically: Change of basis = Multiplication by a fixed matrix T Equivalent definition: Summation form x= ..."
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Very important class of functions: signal processing, scientific computing, … Mathematically: Change of basis = Multiplication by a fixed matrix T Equivalent definition: Summation form x=
Lineartransform
"... techniques for processing shearwave anisotropy in fourcomponent seismic data ..."
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techniques for processing shearwave anisotropy in fourcomponent seismic data
An analysis of transformations
 Journal of the Royal Statistical Society. Series B (Methodological
, 1964
"... In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedasti ..."
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Cited by 1067 (3 self)
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, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality
Survey on Independent Component Analysis
 NEURAL COMPUTING SURVEYS
, 1999
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 2309 (104 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation
Linear Transformations on Codes
"... This paper studies and classifies linear transformations that connect Hamming distances of codes. These include irreducible linear transformations and their concatenations. Their e#ect on the Hamming weights of codewords is also studied. Both linear and nonlinear codes over fields are considered. W ..."
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This paper studies and classifies linear transformations that connect Hamming distances of codes. These include irreducible linear transformations and their concatenations. Their e#ect on the Hamming weights of codewords is also studied. Both linear and nonlinear codes over fields are considered
Guaranteed minimumrank solutions of linear matrix equations via nuclear norm minimization,”
 SIAM Review,
, 2010
"... Abstract The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and col ..."
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Cited by 562 (20 self)
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for the linear transformation defining the constraints, the minimum rank solution can be recovered by solving a convex optimization problem, namely the minimization of the nuclear norm over the given affine space. We present several random ensembles of equations where the restricted isometry property holds
Studies of transformation of Escherichia coli with plasmids
 J. Mol. Biol
, 1983
"... Factors that affect he probability of genetic transformation f Escherichia coli by plasmids have been evaluated. A set of conditions is described under which about one in every 400 plasmid molecules produces a transformed cell. These conditions include cell growth in medium containing elevated level ..."
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Cited by 1632 (1 self)
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Factors that affect he probability of genetic transformation f Escherichia coli by plasmids have been evaluated. A set of conditions is described under which about one in every 400 plasmid molecules produces a transformed cell. These conditions include cell growth in medium containing elevated
Factoring wavelet transforms into lifting steps
 J. FOURIER ANAL. APPL
, 1998
"... This paper is essentially tutorial in nature. We show how any discrete wavelet transform or two band subband filtering with finite filters can be decomposed into a finite sequence of simple filtering steps, which we call lifting steps but that are also known as ladder structures. This decompositio ..."
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Cited by 584 (8 self)
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This paper is essentially tutorial in nature. We show how any discrete wavelet transform or two band subband filtering with finite filters can be decomposed into a finite sequence of simple filtering steps, which we call lifting steps but that are also known as ladder structures
Representing Coastlines with Linear Transforms
"... Contents 1 Introduction 2 1.1 How to generate coastlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Overview of the project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Linear transforms 5 2.1 Nomenclature . . . . . . . . . . . . . . . . . . . ..."
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Contents 1 Introduction 2 1.1 How to generate coastlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Overview of the project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Linear transforms 5 2.1 Nomenclature
Results 1  10
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