Silence/speech detection method based on set of decision graphs

Abstract

In the paper we demonstrate a complex supervised learning method based on a binary decision graphs. This method is employed in construction of a silence/speech detector. Performance of the resulting silence/speech detector is compared with performance of common silence/speech detectors used in telecommunications and with a detector based on HMM and a bigram silence/speech language model. Each non-leaf node of a decision graph has assigned a question and a sub-classifier answering this question. We test three kinds of these sub-classifiers: linear classifier, classifier based on separating quadratic hyper-plane (SQHP), and Support Vector Machines (SVM) based classifier. Moreover, besides usage of a single decision graph we investigate application of a set of binary decision graphs. © Springer-Verlag Berlin Heidelberg 2006.