Probability based optimization for network classifiers

The authors propose a probability based optimization (PBO) algorithm which exploits the inherent statistical properties present in the speech signal and effectively minimizes the error probability during the classification stages of the system. The performance of this algorithm has been systematically evaluated using a MAP (maximum a posteriori) estimator and a Kohonen (1988) map. In the evaluation of the MAP classifier, the PBO algorithm contributes to a decrease of the recognition error rate with respect to both training and test data. The experimental results on applying PBO to a Kohonen map classifier are compared with the results obtained by using LVQ2, revealing a decrease of the classification error rate for both the broad class database and the vowel database.