This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based 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

Very important class of functions: signal processing, scientific computing, … Mathematically: Change of basis = Multiplication by a fixed matrix T Equivalent definition: Summation form x=

techniques for processing shear-wave anisotropy in four-component seismic data

, 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

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

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 non-linear codes over fields are considered

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

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

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

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