SPEECH RECOGNITION OF ARABIC WORDS USING ARTIFICIAL NEURAL NETWORKS (Journal of the College of Education for Women, 2019)
The speech recognition system has been widely used by many researchers using differentmethods to fulfill a fast and accurate system. Speech signal recognition is a typicalclassification problem, which generally includes two main parts: feature extraction andclassification. In this paper, a new approach to achieve speech recognition task is proposed byusing transformation techniques for feature extraction methods; namely, slantlet transform(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, amodified artificial neural network (ANN) with dynamic time warping (DTW) algorithm isdeveloped to train a speech recognition system to be used for classification and recognitionpurposes. Twenty three Arabic words were recorded fifteen different times in a studioby one speaker to form a database. The performance of the proposed system using thisdatabase has been evaluated by computer simulation using MATLAB package. The resultshows recognition accuracy of 65%, 70% and 80% using DWT (Db1), DWT (Db4) and SLTrespectively.