A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of the compressed signal relative to the size of the uncompressed signal. The proposed algorithms where fulfilled with the use of Matlab package
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. T
تناولنا في بحثنا أحد اساليب البرمجة الخطية وهي الطريقة المبسطة لتقدير معلمات انموذج الانحدار الخطي عن طريق اختيار دالة الهدف التي تعمل على تقليل الحد الادنى لمجموع الاخطاء الناتجة من تقدير المعلمات بطريقة المربعات الصغرى الاعتيادية ( OLS) حيث سيتم في الطريقة المبسطة ( simplex) فرض قيود على نفس الاخطاء نفسها بهدف تصغيرها الى اقل ما يمكن للحصول على تقديرات افضل لمعلمات انموذج الانحدار الخطي . على اساس ان طريقة المرب
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The final
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