On the predictive connectionist models for automatic speech recognition

This paper addresses the equivalence of mapping functions between linked predictive neural networks (LPNN) and hidden control neural networks (HCNN). Two theoretical results supported by Mathematica experiments are presented. First, it is proved that for every HCNN model there exist an equivalent LPNN model. Second, it is shown that the set of input-output functions of an LPNN model is strictly larger than the set of functions of an equivalent HCNN model. Therefore, when using equal architecture of the canonical building blocks (MLPs) for the LPNN and HCNN models, the LPNN models represent a superset of the approximation capabilities of the HCNN models. On the other hand, comparative experiments on the same task showed that the HCNN system outperforms the LPNN system