Ensemble methods for connectionist acoustic modelling

In this paper we i n v estigate 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-of-experts. These techniques have been applied to multi-layer 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.