A comparison of combined classifier architectures for Arabic Speech Recognition

Combined classifiers offer solution to the pattern classification problems which arise from variation of the data acquisition conditions, the signal representing the pattern to be recognized and classifier architecture itself. This paper studies the effect of classifier architecture on the overall performance of the Arabic Speech Recognition System. Five different proposed combined classifier architectures are studied and a comparison of their performance is conducted. Boosting is another type of combined classifier to improve the performance of almost any learning algorithm. We investigate the effect of combining Neural Networks by AdaBoost.M1 and propose an enhancement for AdaBoost.M1 algorithm. It is found that the proposed enhanced AdaBoost.M1 outperforms either the architectures based on ensemble approaches or the modular approaches.