Exploiting prediction error in a predictive-based connectionist speech recognition system

The authors show how to exploit additionally a prediction error signal in the context-dependent hidden control neural network (HCNN-CDF) continuous speech recognition (SR) system to increase the discrimination among predictive models of the system. First, by using linear discriminant analysis (LDA) they analyze the squared prediction errors of the system on a SR task. The results clearly show that the residual prediction error signal contains information further to support discrimination among the models of the system. LDA also determines which components of the residual prediction error signal contribute most to discrimination among the models. It is used as a tool to determine the dimensionality of the predicted error vector to be modeled. Second, using the results from discriminant analysis, a new HCNN model which predicts (i.e., computes) the squared prediction error signal from the speech data is proposed. Using these HCNN models, an increased discrimination among predictive models of the system was observed.