Ensemble Methods for Phoneme Classification

Abstract

In this paper we investigate a number of ensemble methods for improving the performance of phoneme classification for use in a speech recognition system. We discuss boosting and mixtures of experts, both in isolation and in combination. We present results on an isolated word database. The results show that principled ensemble methods such as boosting and mixtures provide superior performance to more naive ensemble methods. When used in combination, boosting and mixtures provide a further improvement in performance. Keywords: Speech Recognition, Boosting, Mixtures of Experts. INTRODUCTION There is now considerable interest in using ensembles or committees of learning machines to improve the performance of the system over that of a single learning machine. In most neural network ensembles, the ensemble members are trained on either the same data (Hansen & Salamon 1990) or different subsets of the data (Perrone & Cooper 1993). The ensemble members typically have different initial condi...