Recognition System for Nasal, Lateral and Trill Arabic Phonemes using Neural Networks

There has been limited study and research in Arabic phoneme among Malaysians, hence making references to the work and research difficult. Although there have been significant acoustic and phonetic studies on languages such as English, French and Mandarin, to date there are no guidelines or significant findings on Malay language. In this paper, we monitored and analyzed the performance of multi-layer feed-forward with back-propagation (MLFFBP) and cascade-forward (CF) networks on our phoneme recognition system of Standard Arabic (SA). This study focused on Malaysian children as test subjects. Focused on four chosen phonemes from SA, which composed of nasal, lateral and trill behaviors, i.e. tabulated at four different articulation places. Highest training recognition rate for multi-layer and cascade-layer network are 98.8 % and 95.2 % respectively, while the highest testing recognition rate achieved for both networks is 92.9 %. 10-fold cross validation was used to evaluate system performance. The selected network is cascade layer with 40 and 10 hidden neurons in first hidden layer and second hidden layer respectively. The chosen network was used in the GUI designed for developing recognition system with user feedback.