Abstract
This paper focuses on a machine learning that learn the correct pronunciation Arabic phonemes. In this study, the researchers develop using convolutional neural network as feature extraction in order to enhance the performance of the model and Multi layer perceptron as the classifier to classify classes. Different parameters of CNN model are used in order to investigate the best parameter for the recognition purpose. The dataset have been recorded from experts using smartphone which consist of 880 recorded audios to train the model (210 for each class). The researchers have experimented the models to measure the accuracy and the cross entropy in the training process.
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Mazlin, I., Nasruddin, Z.A., Adnan, W.A.W., Razak, F.H.A. (2019). Arabic Phonemes Recognition Using Convolutional Neural Network. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_21
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DOI: https://doi.org/10.1007/978-981-15-0399-3_21
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