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Entropy 2018, 20(8), 600; https://doi.org/10.3390/e20080600

Identity Vector Extraction by Perceptual Wavelet Packet Entropy and Convolutional Neural Network for Voice Authentication

School of Information and Software Engineering, University of Electrical and Science and Technology of China, Chengdu 610054, China
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Received: 25 June 2018 / Revised: 2 August 2018 / Accepted: 9 August 2018 / Published: 13 August 2018
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory III)
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Abstract

Recently, the accuracy of voice authentication system has increased significantly due to the successful application of the identity vector (i-vector) model. This paper proposes a new method for i-vector extraction. In the method, a perceptual wavelet packet transform (PWPT) is designed to convert speech utterances into wavelet entropy feature vectors, and a Convolutional Neural Network (CNN) is designed to estimate the frame posteriors of the wavelet entropy feature vectors. In the end, i-vector is extracted based on those frame posteriors. TIMIT and VoxCeleb speech corpus are used for experiments and the experimental results show that the proposed method can extract appropriate i-vector which reduces the equal error rate (EER) and improve the accuracy of voice authentication system in clean and noisy environment. View Full-Text
Keywords: i-vector; wavelet entropy; speaker authentication; CNN i-vector; wavelet entropy; speaker authentication; CNN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lei, L.; She, K. Identity Vector Extraction by Perceptual Wavelet Packet Entropy and Convolutional Neural Network for Voice Authentication. Entropy 2018, 20, 600.

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