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Deep Learning in Speaker Recognition

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Development and Analysis of Deep Learning Architectures

Part of the book series: Studies in Computational Intelligence ((SCI,volume 867))

Abstract

It is supposed in Speaker Recognition (SR) that everyone has a unique voice which could be used as an identity rather than or in addition to other identities such as fingerprint, face, or iris. Even though steps have been taken long ago to apply neural networks in SR, recent advances in computing hardware, new deep learning (DL) architectures and training methods, and access to a large amount of training data have inspired the research community to make use of DL as in a large variety of other signal processing applications. In this chapter, the traditional principle techniques in SR are first briefly reviewed and the potential signal processing aspects of these techniques which can be improved by DL are addressed. Then the recent most successful DL architectures used in SR are introduced and some illustrative experiments from the authors are included.

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Acknowledgements

This work is partially supported by the Spanish project DeepVoice under grant number TEC2015-69266-P.

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Authors and Affiliations

  1. EML European Media Laboratory GmbH, Heidelberg, Germany

    Omid Ghahabi

  2. Universitat Politècnica de Catalunya - BarcelonaTech, Barcelona, Spain

    Pooyan Safari & Javier Hernando

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  1. Omid Ghahabi
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  2. Pooyan Safari
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  3. Javier Hernando
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Correspondence to Omid Ghahabi .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Ghahabi, O., Safari, P., Hernando, J. (2020). Deep Learning in Speaker Recognition. In: Pedrycz, W., Chen, SM. (eds) Development and Analysis of Deep Learning Architectures. Studies in Computational Intelligence, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-31764-5_6

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