A modified mixtures of experts architecture for classification with diverse features

A modular neural architecture, MME, is considered here as an alternative to the standard mixtures of experts architecture for classification with diverse features. Unlike the standard mixtures of experts architecture, a gate-bank consisting of multiple gating networks is introduced to the proposed architecture, and those gating networks in the gate-bank receive different input vectors while expert networks may be receiving different input vectors. As a result, a classification task with diverse features can be learned by the modular neural architecture through the use of different features simultaneously. In, the proposed architecture, learning is treated as a maximum likelihood problem and an EM algorithm is presented for adjusting the parameters of the architecture. Comparative simulation results are presented for a real world problem called text-dependent speaker identification.