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Speaker Recognition Anti-spoofing

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Handbook of Biometric Anti-Spoofing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Progress in the development of spoofing countermeasures for automatic speaker recognition is less advanced than equivalent work related to other biometric modalities. This chapter outlines the potential for even state-of-the-art automatic speaker recognition systems to be spoofed. While the use of a multitude of different datasets, protocols and metrics complicates the meaningful comparison of different vulnerabilities, we review previous work related to impersonation, replay, speech synthesis and voice conversion spoofing attacks. The article also presents an analysis of the early work to develop spoofing countermeasures. The literature shows that there is significant potential for automatic speaker verification systems to be spoofed, that significant further work is required to develop generalised countermeasures, that there is a need for standard datasets, evaluation protocols and metrics and that greater emphasis should be placed on text-dependent scenarios.

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Notes

  1. 1.

    http://www.nuance.com/landing-pages/products/voicebiometrics/freespeech.asp.

  2. 2.

    http://www.tabularasa-euproject.org/.

  3. 3.

    http://www.tabularasa-euproject.org/.

  4. 4.

    In practice samples labelled as spoofing attacks cannot be fully discarded since so doing would unduly influence false reject and false acceptance rates calculated as a percentage of all accesses.

  5. 5.

    Produced with the TABULA RASA Score-toolkit: http://publications.idiap.ch/downloads/reports/2012/Anjos_Idiap-Com-02-2012.pdf.

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Acknowledgments

This work was partially supported by the TABULA RASA project funded under the 7th Framework Programme of the European Union (EU) (grant agreement number 257289), by the Academy of Finland (project no. 253120) and by EPSRC grants EP/I031022/1 (NST) and EP/J002526/1 (CAF).

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Authors and Affiliations

  1. Department of Multimedia Communications, Campus SophiaTech, EURECOM, 450 Route des Chappes, 06410, Biot, France

    Nicholas Evans & Federico Alegre

  2. Speech and Image Processing Unit, School of Computing, University of Eastern Finland (UEF), P.O. Box 111, FI-80101, Joensuu, Finland

    Tomi Kinnunen

  3. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

    Junichi Yamagishi

  4. University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, UK

    Junichi Yamagishi

  5. Emerging Research Lab, School of Computer Engineering, Nanyang Technological University (NTU), N4-B1a-02 C2I, Nanyang Avenue, Singapore, 639798, Singapore

    Zhizheng Wu

  6. Department 3-O, Klipsch School of Electrical and Computer Engineering, New Mexico State University, PO Box 30001 , Las Cruces, NM, 88003-8001, USA

    Phillip De Leon

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  1. Nicholas Evans
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  2. Tomi Kinnunen
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  3. Junichi Yamagishi
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  4. Zhizheng Wu
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  5. Federico Alegre
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  6. Phillip De Leon
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Correspondence to Nicholas Evans .

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Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, United Kingdom

    Mark S. Nixon

  3. Institute of Automation Center for Biometrics and Security Resea, Chinese Academy of Sciences, Beijing, China

    Stan Z. Li

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Evans, N., Kinnunen, T., Yamagishi, J., Wu, Z., Alegre, F., Leon, P. . (2014). Speaker Recognition Anti-spoofing. In: Marcel, S., Nixon, M., Li, S. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6524-8_7

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