Speaker recognition using convolutional siamese neural networks

Recently, machine learning has been applied in a variety of fields. Speaker recognition is one of attractive applications of machine learning. In this paper, we propose a convolutional Siamese neural network for speaker recognition. The proposed model generates feature vectors through the identical two convolutional neural networks for speech data of two speakers. The similarity is measured by calculating the Euclidean distance of two output feature vectors. If the calculated similarity is less than the threshold, it is judged that two speakers are the same. The experimental result of the proposed speaker recognition based on the convolutional Siamese neural network showed its accuracy was achieved up to 96%. The accuracy of one-shot classification using the trained convolutional Siamese neural network was evaluated also. For the evaluation, the 10-way one-shot classification for 10 speakers not used for learning stages were tested, resulting in 92% accuracy. © 2020 Korean Institute of Electrical Engineers. All rights reserved.