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Balancing validity and reliability as a function of sampling variability in forensic voice comparison.

Bruce Xiao Wang1, Vincent Hughes2

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Summary
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Forensic voice comparison using advanced automatic speaker recognition (ASR) systems shows improved validity but not necessarily reliability. High validity can paradoxically decrease reliability, especially with fewer speakers in forensic voice comparison (FVC) cases.

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Area of Science:

  • Forensic Science
  • Speech Technology
  • Biometrics

Background:

  • Forensic comparison sciences require valid and reliable methods for evaluating evidence from known and unknown sources.
  • Legal standards mandate the use of scientifically sound forensic methods, as emphasized by major scientific bodies.
  • Forensic voice comparison (FVC) is a critical area within forensic science.

Purpose of the Study:

  • To examine the impact of speaker quantity and sampling variability on the validity and reliability of automatic speaker recognition (ASR) systems in FVC.
  • To compare different generations of ASR systems for their performance in forensic voice comparison.
  • To understand the relationship between system validity and reliability in FVC.

Main Methods:

  • Simulation study examining the effects of speaker numbers and sampling variability.
  • Evaluation of different generations of automatic speaker recognition (ASR) systems.
  • Analysis of validity and reliability metrics in forensic voice comparison (FVC) scenarios.

Main Results:

  • State-of-the-art ASR systems demonstrated better overall validity than older systems.
  • Improved system validity did not consistently lead to higher reliability; often, the opposite was observed.
  • Higher validity and discriminability can increase output uncertainty and inconsistency, reducing reliability, particularly with limited speaker data.

Conclusions:

  • While advanced ASR systems offer enhanced validity in FVC, their reliability can be compromised.
  • The trade-off between validity and reliability is a significant consideration in forensic voice comparison.
  • Extrapolation due to insufficient data in small speaker pools can lead to unreliable outcomes in FVC casework.