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Identifying Speakers Using Deep Learning: A review

Author

Listed:
  • Lawchak Fadhil Khalid

    (Technical College of Informatics - Akre, Duhok Polytechnic University (DPU), Kurdistan Region, Iraq)

  • Adnan Mohsin Abdulazeez

    (Duhok Polytechnic University (DPU), Kurdistan Region, Iraq)

Abstract

With the advancement of technology and the increasing demand on smart systems and smart applications that provide a quality-of-life improvement, there has been a surge in the demand of more conscious applications, Machine Learning (ML) is considered one of the driving forces behind implementing these types of applications, and one of its implementations is Speaker Identification (SID). Deep Neural Networks (DNNs) and also Recurrent Neural Networks (RNNs) are two main types of Deep Learning that are being used in the implementation of such applications. Speaker Identification is being utilized more and more on daily basis and is being focused on by the research community as a result of this demand. In this paper, a review will be conducted to some of the most recent researches that were conducted in this area and compare their results while discussing their outcomes.

Suggested Citation

  • Lawchak Fadhil Khalid & Adnan Mohsin Abdulazeez, 2021. "Identifying Speakers Using Deep Learning: A review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 15-26.
    • Handle: RePEc:aif:journl:v:5:y:2021:i:3:p:15-26
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    References listed on IDEAS

    as
    1. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, July.
    2. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, November.
    3. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
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