Event Selection from Phone Posteriorgrams Using Matched Filters

In this paper we address the issue of how to select a minimal set of phonetic events from a phone posteriorgram while minimizing the loss of information. We derive phone posteriorgrams from two sources, Gaussian mixture models and sparse multilayer perceptrons, and apply phone-specific matched filters to the posteriorgrams to yield a smaller set of phonetic events. We introduce a mutual information based performance measure to compare phonetic event selection techniques and demonstrate that events extracted using matched filters can reduce input data while significantly improving performance of an event-based keyword spotting system.