Biometric Evidence in Forensic Automatic Speaker Recognition

  • Andrzej DrygajloEmail author
  • Rudolf Haraksim
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


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.


Likelihood Ratio Gaussian Mixture Model Processing Chain Speaker Recognition Universal Background Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Swiss Federal Institute of Technology Lausanne (EPFL)LausanneSwitzerland

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