مجلة كلية التربية للبنات (Feb 2019)
SPEECH RECOGNITION OF ARABIC WORDS USING ARTIFICIAL NEURAL NETWORKS
Abstract
The speech recognition system has been widely used by many researchers using different methods to fulfill a fast and accurate system. Speech signal recognition is a typical classification problem, which generally includes two main parts: feature extraction and classification. In this paper, a new approach to achieve speech recognition task is proposed by using transformation techniques for feature extraction methods; namely, slantlet transform (SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is developed to train a speech recognition system to be used for classification and recognition purposes. Twenty three Arabic words were recorded fifteen different times in a studio by one speaker to form a database. The performance of the proposed system using this database has been evaluated by computer simulation using MATLAB package. The result shows recognition accuracy of 65%, 70% and 80% using DWT (Db1), DWT (Db4) and SLT respectively.