Probabilistic decision-based neural networks for speech pattern classification

Probabilistic decision-based neural networks (PDBNNs) were originally proposed by Lin, Kung and Lin for human face recognition. Although high recognition accuracy has been achieved, not many illustrations were given to highlight the characteristic of the decision boundaries. This paper aims at providing detailed illustrations to compare the decision boundaries of PDBNNs with that of Gaussian mixture models through a pattern recognition task, namely the classification of two-dimensional vowel data. The original PDBNNs use elliptical basis functions with diagonal covariance matrices, which may be inefficient for modeling feature vectors with correlated components. This paper attempts to tackle this problem by using full covariance matrices. The paper also highlights the strengths of PDBNNs by demonstrating that the PDBNN's thresholding mechanism is very effective in rejecting data not belonging to any known classes.