Correlative training and recurrent network automata for speech recognition

Discriminative training is one of the more distinctive features of multilayer perceptron networks when used as classifiers. Although, when dealing with overlapping classes, it may be useful to smooth this feature not compelling the MLP to discrimination where it is impossible. This can be done adaptively, without any prior information about the classes by introducing a straightforward modification of backpropagation, named correlative training. This new MLP feature has proved to be very useful when training the hybrid recurrent network automata model for speech recognition.<
>