Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.
يتطلب تحقيق تمايز الوحدة الاقتصادية في ظل استعمال تقنيات الأعمال الحديثة وازدياد المنافسة وعالمية الأعمال ضرورة الاهتمام بمستوى نوعية المنتجات وما تتطلبه هذه النوعية من كلف والتي تسمى بكلف النوعية، إذ ان العديد من الشركات العالمية قد قامت بدراسة وتحليل هذه الكلف ووضع برامج خاصة بها بهدف تخفيضها إلى أدنى حدٍ ممكن وبما يكفل تحقيق العديد من المنافع والتوفيرات في هذه الكلف وبما يرشد عملية اتخاذ القرارات
... Show MoreProducts’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers. In this research, we pr
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