In this paper we present a novel approach for estimating featurespace maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maxim...
Arnab Ghoshal, Daniel Povey, Mohit Agarwal, Pinar ...
This paper aims at providing a better insight into the 3D approximations of the wave equation using compact finite-difference timedomain (FDTD) schemes in the context of room aco...
Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computati...
Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yas...
Compressive sensing (CS) is an emerging approach for acquisition of signals having a sparse or compressible representation in some basis. While CS literature has mostly focused on...
In this work, we show how expectation maximization based simultaneous channel and noise estimation can be derived without a vector Taylor series expansion. The central idea is to ...
Friedrich Faubel, John W. McDonough, Dietrich Klak...