Tunisian Dialect Recognition Based on Hybrid Techniques

In this research paper, an Arabic Automatic Speech Recognition System is implemented in order to recognize ten Arabic digits (from zero to nine) spoken in Tunisian dialect (Darija). This system is divided in two main modules: The feature extraction module by combining a few conventional feature extraction techniques, and the recognition module by using FeedForward Back Propagation Neural Networks (FFBPNN). For this purpose, four oral proper corpora are prepared by five speakers each. Each speaker pronounced the ten digits five times. The chosen speakers are different in gender, age and physiological conditions. We focus our experiments on a speaker dependent system and we also examined the case of speaker independent system. The obtained recognition performances are almost ideal and reached up to 98.5% when we use for the feature extraction phase the Perceptual Linear Prediction technique (PLP) followed firstly by its first-order temporal derivative (∆PLP ) and secondly by Vector Quantization of Linde-Buzo-Gray (VQLBG).