International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume3,May 2016,

Topic : An Investigation of End-To-End Speaker Recognition Using Deep Neural Networks

Authors:Lakhsmi HR || Sivanand Achanta , Suryakanth V Gangashetty , R Kumaraswamy

Abstract:State-of-the-art automatic speaker recognition (SR) has been dominated by Gaussian mixture model-universal background model (GMM-UBM) based i-vector feature extraction methods. Although these systems are robust, extraction of ivectors is very time consuming and a separate classifier needs to be trained for decision making in the end. Inorder to alleviate the above disadvantages, in this paper we propose to use deep neural networks for end-to-end speaker recognition. We perform several experiments to determine the best suited architecture, the hyper-parameter tuning algorithm and the initialization scheme for SR task. The proposed method combines feature extraction and classification step, and is of very low foot print. Through objective metric (equal error rate) we show that the proposed method outperforms the GMM-UBM conventional system.

Keywords: Speaker Recognition ,Neural Networks , i-vector

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DOI: 01.1617/vol3iss5pid208


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Editor-in-Chief

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Dr.Zhongfu Tan
National Professor,
North China Electric Power University,
CHINA


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IMPACT FACTOR: 3.689

ISSN(Online): 2394-6849

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERECE
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