The optimization of Artificial Neural Networks connection weights using genetic algorithms for isolated spoken Malay parliamentary speeches

This paper presents the structure of a neural network models for validation recognition performances of isolated spoken Malay utterances. Artificial Neural Network (ANN) has been well recognized for its approximation capability provided the input-output data are available. Nevertheless, the conventional training algorithm, Levenberg-Marquardt (LM) algorithms that utilized as gradient search method in the model development has always encountered difficulties to converge at global solution. Aiming at improving the accuracy and robustness of ANN model, Genetic Algorithm (GA) was introduced in ANN modelling for connection weights evolution. From the results, it was observed that the performance of GA-ANN models is better than ANN-LM models. Integrating the GA with feedforward network can improve mean square error (MSE) performance and by this two stage training scheme, the recognition rate can be increased up to 85%.