(untitled) (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.