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Person identification based on voice biometric using deep neural network

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Abstract

Nowadays in all everyday transactions, technological progress has become an intrinsic characteristic that depends on such electronic applications as financial and banking transfers, health care, project management, and other crucial life aspects.  The core of these applications is person Identification and/or verification steps which can be considered one of the complicated limitations.  Accordingly, the employment of biometric attributes can yield promising outcomes in these fields.  A One’s voice is a unique bio-feature whereby people can be authenticated and precludes others from assuming a one’s identity without their previous knowing or assent.  This work proposes a model with a new architecture to identify the person by exploiting the unique individual characteristics available in one’s voice based on deep learning. An augmentation method is utilized to increase the samples in the available dataset. The available temporal information at an input audio file is analysed then feature maps from this information are extracted which represent the salient temporal feature (time-domain features).  The decision is made based on tracking these voice features over time. Successful and promising results are achieved through this work, the accuracy is close to 99.81% (± 1.78%) and the values of loss function are close to 0.009 over VoxCeleb1 dataset for identifying 40 subjects.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq

    Noor D. AL-Shakarchy & Zahraa Najm Abdullah

  2. College of Education for Humanities Studies, University of Babylon, Babylon, Iraq

    Hadab Khalid Obayes

Authors
  1. Noor D. AL-Shakarchy
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  2. Hadab Khalid Obayes
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  3. Zahraa Najm Abdullah
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Correspondence to Noor D. AL-Shakarchy.

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AL-Shakarchy, N.D., Obayes, H. & Abdullah, Z.N. Person identification based on voice biometric using deep neural network. Int. j. inf. tecnol. 15, 789–795 (2023). https://doi.org/10.1007/s41870-022-01142-1

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