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Data strategies in forensic automatic speaker comparison.

David van der Vloed1

  • 1Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB The Hague, the Netherlands.

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Summary

Automatic speaker recognition (ASR) in forensic speaker comparison requires sufficient data. A new strategy using only 30 speakers performs comparably to the traditional 60-speaker method, making ASR more accessible.

Keywords:
Automatic speaker recognitionForensic caseworkForensic speaker comparisonForensic voice comparisonRepresentative data

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Area of Science:

  • Forensic Science
  • Speech Technology
  • Biometrics

Background:

  • Automatic speaker recognition (ASR) is crucial for forensic speaker comparison (FSC).
  • ASR requires representative audio data for reference normalization and score-to-LR function training.
  • Stable ASR performance necessitates a minimum number of speakers, traditionally 30 for calibration and 60 for both calibration and normalization.

Purpose of the Study:

  • To investigate data strategies for ASR in FSC when only 30 speakers are available.
  • To evaluate methods for overcoming data limitations in forensic speaker recognition.
  • To determine if reduced data requirements can maintain or improve ASR performance in FSC.

Main Methods:

  • Simulated a scenario with only 30 available speakers.
  • Tested data strategies: omitting reference normalization, splitting speakers into smaller groups, and a leave-1-or-2-out approach.
  • Compared these strategies against a baseline using the required 60 speakers.

Main Results:

  • The leave-1-or-2-out strategy with 30 speakers performed comparably to the baseline (60 speakers).
  • Extending the leave-1-or-2-out strategy to 60 speakers resulted in performance exceeding the baseline.
  • The proposed strategies significantly reduce the data requirements for ASR in FSC.

Conclusions:

  • A viable data strategy can halve the data needs for ASR in forensic speaker comparison.
  • This approach makes ASR more feasible in FSC casework with limited speaker data.
  • Reduced data requirements enhance the applicability of ASR in forensic contexts.