Directed graphical models of classifier combination: application to phone recognition
Jeff Bilmes, Katrin Kirchhoff · 2000
Classifier combination is a technique that often provides appreciable accuracy gains. In this paper, we argue that the underlying statistical model of classifier combination should be made explicit. Using directed graphical models (DGMs), we provide representations of two common combination schemes, the mean and product rules. We also introduce new DGMs that yield novel combination rules. We find that these new DGM-inspired rules can achieve significant accuracy gains on the TIMIT phone-classification task relative to existing combination schemes. 1. INTRODUCTION When multiple independently trained pattern classifiers are combined, the resulting accuracy is often better than any of the individual classifiers. This has been demonstrated for automatic speech recognition (ASR) [7, 10, 18] and for pattern classification [12, 13, 20, 29]. Classifier combination can fuse together different information sources to utilize their complementary information. The sources can be multi-modal, such...