Showing papers by "Richard P. Lippmann published in 1992"

A Boundary Hunting Radial Basis Function Classifier which Allocates Centers Constructively

Abstract: A new boundary hunting radial basis function (BH-RBF) classifier which allocates RBF centers constructively near class boundaries is described. This classifier creates complex decision boundaries only in regions where confusions occur and corresponding RBF outputs are similar. A predicted square error measure is used to determine how many centers to add and to determine when to stop adding centers. Two experiments are presented which demonstrate the advantages of the BH-RBF classifier. One uses artificial data with two classes and two input features where each class contains four clusters but only one cluster is near a decision region boundary. The other uses a large seismic database with seven classes and 14 input features. In both experiments the BH-RBF classifier provides a lower error rate with fewer centers than are required by more conventional RBF, Gaussian mixture, or MLP classifiers.

Comparison of auditory models for robust speech recognition

Abstract: Two auditory front ends which emulate some aspects of the human auditory system were compared using a high performance isolated word Hidden Markov Model (HMM) speech recognizer. In these initial studies, auditory models from Seneff [2] and Ghitza [4] were compared using both clean speech and speech corrupted by speech-like "babble" noise. Preliminary results indicate that the auditory models reduce the error rate slightly, especially at intermediate and high noise levels.