Steve R. Waterhouse

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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 Percept ron (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 hybrid connectionist-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 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(More)
abbot is the hybrid connectionist hidden Markov model (HMM) large vocabulary continuous speech recognition system developed at Cambridge University Engineering Department. abbot makes e ective use of the linear input network (LIN) adaptation technique to achieve speaker and channel adaptation. Although the LIN is e ective at adapting to new speakers or a(More)