September 22-25, 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-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.
Bibliographic reference. Cook, Gary D. / Waterhouse, Steve R. / Robinson, A.J. (1997): "Ensemble methods for connectionist acoustic modelling", In EUROSPEECH-1997, 1959-1962.