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We present a Bayesian framework for inferring the parameters of a mixture of experts model based on ensemble learning by varia-tional free energy minimisation. The Bayesian approach avoids the over-fitting and noise level underestimation problems of traditional maximum likelihood inference. We demonstrate these methods on artificial problems and sunspot(More)
We present two additions to the hierarchical mixture of experts (HME) architecture. We view the HME as a tree structured clas-siier. Firstly, by applying a likelihood splitting criteria to each expert in the HME we \grow" the tree adaptively during training. Secondly, by considering only the most probable path through the tree we may \prune" branches away,(More)
In this paper we consider speech coding as a problem of speech modelling. In particular, prediction of parameterised speech over short time segments is performed using the Hierarchical Mixture of Experts (HME) (Jordan & Jacobs 1994). The HME gives two advantages over traditional non-linear function approximators such as the Multi-Layer Perceptron (MLP); a(More)
This paper describes the development of the cu-con system which participated in the 1996 ARPA Hub 4 Evaluations. The system is based on Abbot, a h ybrid connec-tionist-HMM large vocabulary continuous speech recognition system developed at the Cambridge University Engineering Department [4]. The Hub 4 Evaluation task involves the transcription of broadcast(More)
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(More)