Sparse Kernel Logistic Regression using Incremental Feature Selection for Text-Independent Speaker Identification

Logistic regression is a well known classification method in the field of statistical learning. Recently, a kernelized version of logistic regression has become very popular, because it allows non-linear probabilistic classification and shows promising results on several benchmark problems. In this paper we show that kernel logistic regression (KLR) and especially its sparse extensions (SKLR) are useful alternatives to standard Gaussian mixture models (GMMs) and support vector machines (SVMs) in Speaker recognition. While the classification results of KLR and SKLR are similar to the results of SVMs, we show that SKLR produces highly sparse models. Unlike SVMs the kernel logistic regression also provides an estimate of the conditional probability of class membership. In speaker identification experiments the SKLR methods outperform the SVM and the GMM baseline system on the POLY-COST database