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.
In the name of God, the Most Gracious, the Most Merciful
Praise be to God who revived Balu. And the good word brought hard hearts after removing from them what had almost killed them of ignorance, injustice, hatred, hostility, estrangement, and irritation... So God replaced for them what was best with what was best in terms of knowledge and fairness, and what was corrupt, love, and altruism. So the example of a believer and a good word is like a tree with good roots and branches. Then I pray and greet the one who watered it from the pure and inexhaustible spring of Islam, our Master Muhammad, so that the valleys flowed in their capacity, and the crops grew, then they became thicker and became even, and their good fruits in behavi
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