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

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

We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a

Orthogonal wavelet transforms have recently become a popular representation for multiscale signal and image analysis. One of the major drawbacks of these representations is their lack of translation invariance: the content of wavelet subbands is unstable under translations of the input signal

based on linear transformations, i.e. a special type of spectral techniques, has been proposed. In this paper we study this minimization method in more detail. While so far only experimental results are known, we prove for a family of Boolean functions that by linear transformations an exponential