Comparison of weights connection strategies for spoken Malay speech recognition system

This paper presents the comparison performance of weights connection strategies approaches between artificial neural network (ANN), conjugate gradient (CG) learning algorithms with genetic algorithms (GA) method for acoustic modelling speech recognition system. Both methods are used to find the optimum weights for the hidden and output layers of artificial neural network (ANN) model. Each algorithm is presented in separate module and we proposed three different types of Weights Connection Strategies for combining both algorithms to improve the recognition performance of spoken Malay speech recognition. Two different GA techniques are used in this research: a mutated GA (mGA) technique is proposed and compared with the standard GA technique. One hundred experiments with 5000 words are conducted using the proposed strategies. Owing to previous facts, GA combined with ANN proved to attain certain advantages with sufficient recognition performance. Thus, from the results, it was observed that the performance of mutated GA algorithm when combined with CG is better than standard GA and CG models. Integrating the GA with feed-forward network improved mean square error (MSE) performance and with good connection strategy by this two stage training scheme, the recognition rate is increased up to 99%.