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Efficient Gender Classifier for Arabic Speech Using CNN With Dimensional Reshaping

Nowadays, gender detection is an open research in the field of soft biometrics. Human gender detection can be implemented depending on a voice or a speech. In this paper, an improved recognition rate of an individual gender detection is approached. The methodology starts with a proposed pre-processing technique, which is converting 1-dimensional (1D) into 3-dimensional (3D) data voice. Then the whole 3D voice samples are passed to the convolutional neural network for both training and testing (predicting) the class whether it is a male or a female. The number of the convolutional neural network (CNN) layers is 19 distributed into 4 rounds, in which each round contains sub-layers. The performance is extracted by using a database for Arabic speech that contains 6644 voice samples. The average result of the recognition rate is achieved up to 98.91%, which outperforms the state of the art using the same database.