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SPEECH RECOGNITION OF ARABIC WORDS USING ARTIFICIAL NEURAL NETWORKS | ||
| JOURNAL OF THE COLLEGE OF EDUCATION FOR WOMEN | ||
| Article 1, Volume 25, Issue 1, 2014, Pages 196-206 | ||
| Author | ||
| Sadiq Jassim Abou-Loukh | ||
| 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. | ||
| Keywords | ||
| Speech Recognition; Discrete Wavelet Transform; Slantlet Transform Dynamic Time Warping; artificial neural network | ||
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