Recognition of Arabic phonetic features using neural networks and knowledge-based system: a comparative study

This paper deals with a new indicative features recognition system for Arabic which uses a set of a simplified version of sub-neural-networks (SNN). For the analysis of speech, the perceptual linear predictive technique is used. The ability of the system has been tested in experiments using stimuli uttered by 6 native Algerian speakers. The identification results have been confronted to those obtained by the SARPH knowledge based system. Our interest goes to the particularities of Arabic such as geminate and emphatic consonants and the duration. The results show that SNN achieved well in pure identification while in the case of phonologic duration the knowledge-based system performs better.