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Biometric Evidence in Forensic Automatic Speaker Recognition

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Handbook of Biometrics for Forensic Science

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

Abstract

The goal of this chapter is to provide a methodology for calculation and interpretation of biometric evidence in forensic automatic speaker recognition (FASR). It defines processing chains for observed biometric evidence of speech (univariate and multivariate) and for calculating a likelihood ratio as the strength of evidence in the Bayesian interpretation framework. The calculation of the strength of evidence depends on the speaker models and the similarity scoring used. A processing chain chosen for this purpose is in the close relation with the hypotheses defined in the Bayesian interpretation framework. Several processing chains are proposed corresponding to the scoring and direct method, which involve univariate and multivariate speech evidence, respectively. This chapter also establishes a methodology to evaluate performance of a chosen FASR method under operating conditions of casework.

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Author information

Authors and Affiliations

  1. Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Andrzej Drygajlo & Rudolf Haraksim

Authors
  1. Andrzej Drygajlo
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  2. Rudolf Haraksim
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Correspondence to Andrzej Drygajlo .

Editor information

Editors and Affiliations

  1. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. University of Lausanne, Lausanne, Switzerland

    Christophe Champod

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Drygajlo, A., Haraksim, R. (2017). Biometric Evidence in Forensic Automatic Speaker Recognition. In: Tistarelli, M., Champod, C. (eds) Handbook of Biometrics for Forensic Science. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50673-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-50673-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50671-5

  • Online ISBN: 978-3-319-50673-9

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