CASCADE-FORWARD NEURAL NETWORKS FOR ARABIC PHONEMES BASED ON K-FOLD CROSS VALIDATION (original) (raw)

In this paper, we monitored and analyzed the performance of cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. It is focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. The method, k-fold cross validation to evaluate each network architecture in k times to improve the reliability of the choice of the optimal architecture. Based on k-fold cross validation method, namely 10-fold cross validation, the most suitable cascade-layer network architecture in first hidden layer and second hidden layer is 50 and 30 nodes respectively with MSE less than 0.06. The training and testing recognition rate achieved were 91.6 % and 89.3 % respectively.