New Restricted Boltzmann Machines and Deep Belief Networks for Audio Classification

In this paper, the deep belief network (DBN), made popular by Hinton in 2006, is re-vitalized using maximum entropy sampling distributions, their corresponding activation functions, and a new direct training approach based on classifier performance. It is shown in keyword classification experiments that the DBN can compete with state of the art classifiers, and using additive classifier combination, improves upon a state of the art deep neural network.