Ensemble methods for connectionist acoustic modelling
Gary Cook, Steve R. Waterhouse, A.J. Robinson · 1997
In this paper we investigate a number of ensemble methods for improving the performance of connectionist acoustic models for large vocabulary continuous speech recognition. We discuss boosting, a data selection technique which results in an ensemble of models, and mixtures-ofexperts. These techniques have been applied to multilayer perceptron acoustic models used to build a hybrid connectionist-HMM speech recognition system. We present results on a number of ARPA benchmark tasks, and show that the ensemble methods lead to considerable improvements in recognition accuracy. 1. INTRODUCTION When developing a classification or prediction system it is common practice to train a number of different models, and to retain the model which exhibits the best performance on a cross-validation data set. However, reports in the statistics and neural network literature suggest that improved performance can be achieved by combining the estimates of all the available models [1, 2, 3, 4]. Systems that...