Dlay SS

Meenakshisundaram S

Woo WL

In this paper we introduce a new framework for expert systems used in real time speech applications. This consists of mixture of experts (MoEs) trained for multiclass classifications problems such as speech. We focus mainly on the generalization issues which are surprisingly ignored in established methods and demonstrate how severe these can be when the framework is drafted as a system. We limit this paper by addressing the issues, presenting the MoE capabilities to overcome and statistical perspective behind the training is briefly presented. Significant leap in the performance is achieved and justified by an impressive 10 % improvement on word recognition rate over the best available frameworks and an impressive 18.082 % over the baseline HMM. Critically the error rate is reduced by 10.61% over other connectionist models and 23.29 % over baseline HMM method. (15 References)

Generalization issues in multiclass classification - new framework using mixture of experts

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