An optimum end-to-end text-independent speaker identification system using convolutional neural network

In recent years, convolutional neural networks (CNNs) have outperformed conventional methods in end-to-end speaker identification (SI) systems. The CNN training time is considerably long due to the need for large amounts of training data and high costs of computation and memory consumption. This paper proposes a new CNN for text-independent SI inspired by the VGG-13 architecture with fewer parameters but an acceptable accuracy. In addition to the proposed CNN, the time complexity and memory cost of network training can be reduced through offline feature extraction by using a short segment of each audio sample and online data augmentation. According to the results on Voxceleb1, the proposed system is more accurate than the other state-of-the-art methods in SI. Therefore, the proposed CNN improved the accuracy and decreased the training time.