8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Fixed-Size Kernel Logistic Regression for Phoneme Classification

Peter Karsmakers (1), Kristiaan Pelckmans (2), Johan Suykens (2), Hugo Van hamme (2)

(1) Katholieke Hogeschool Kempen, Belgium
(2) Katholieke Universiteit Leuven, Belgium

Kernel logistic regression (KLR) is a popular non-linear classification technique. Unlike an empirical risk minimization approach such as employed by Support Vector Machines (SVMs), KLR yields probabilistic outcomes based on a maximum likelihood argument which are particularly important in speech recognition. Different from other KLR implementations we use a Nyström approximation to solve large scale problems with estimation in the primal space such as done in fixed-size Least Squares Support Vector Machines (LS-SVMs). In the speech experiments it is investigated how a natural KLR extension to multi-class classification compares to binary KLR models coupled via a one-versus-one coding scheme. Moreover, a comparison to SVMs is made.

Full Paper

Bibliographic reference.  Karsmakers, Peter / Pelckmans, Kristiaan / Suykens, Johan / hamme, Hugo Van (2007): "Fixed-size kernel logistic regression for phoneme classification", In INTERSPEECH-2007, 78-81.