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.
The research falls in to three sections: the first section has two parts; the first one includes a general description of the research: its problem, aim, limitations, methodology, and the terminological definition. The second part presents historical background of the weekly school speeches in Iraq and their types. Section Two has two parts: the first is about the principles of writing weekly speeches, their elements, and principals, their conditions. As for the second part, it is about introducing four samples of weekly school speeches distributed as two taken from Al-karkh sector and two taken from Al-rusafa'a sector. The third section: analyzing the content of the sample and giving a short historical background abo
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Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage
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This work focuses on the implementation of interfaces for human machine interaction (HMI) for control and monitor of automatic production line. The automatic production line which can performance feeding, transportation, sorting functions.
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