Pavel Kisilev, Daniel Freedman, et al.
ICPR 2012
This paper introduces a new class of nonlinear feature space transformations in the context of Gaussian Mixture Models. This class of nonlinear transformations is characterized by computationally efficient training algorithms. Experimental results with quadratic feature space transforms are shown to yield modestly improved recognition performance in a speech recognition context. The quadratic feature space transforms are also shown to be beneficial in an adaptation setting.
Pavel Kisilev, Daniel Freedman, et al.
ICPR 2012
Sudeep Sarkar, Kim L. Boyer
Computer Vision and Image Understanding
Michelle X. Zhou, Fei Wang, et al.
ICMEW 2013
James E. Gentile, Nalini Ratha, et al.
BTAS 2009