Speaker identification in courtroom contexts – Part III: Groups of collaborating listeners compared to forensic voice comparison based on automatic-speaker-recognition technology

Agnes S. Bali, Nabanita Basu, Philip Weber, Claudia Rosas-Aguilar, Gary Edmond, Kristy A. Martire, Geoffrey Stewart Morrison

Research output: Contribution to journalArticlepeer-review

Abstract

Expert testimony is only admissible in common-law systems if it will potentially assist the trier of fact. In order for a forensic-voice-comparison expert’s testimony to assist a trier of fact, the expert’s forensic voice comparison should be more accurate than the trier of fact’s speaker identification. “Speaker identification in courtroom contexts – Part I” addressed the question of whether speaker identification by an individual lay listener (such as a judge) would be more or less accurate than the output of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology. The present paper addresses the question of whether speaker identification by a group of collaborating lay listeners (such as a jury) would be more or less accurate than the output of such a forensic-voice-comparison system. As members of collaborating groups, participants listen to pairs of recordings reflecting the conditions of the questioned- and known-speaker recordings in an actual case, confer, and make a probabilistic consensus judgement on each pair of recordings. The present paper also compares group-consensus responses with “wisdom of the crowd” which uses the average of the responses from multiple independent individual listeners.
Original languageEnglish
Article number112048
Number of pages13
JournalForensic Science International
Volume360
Early online date6 May 2024
DOIs
Publication statusE-pub ahead of print - 6 May 2024

Bibliographical note

Copyright © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Keywords

  • Admissibility
  • Forensic voice comparison
  • Likelihood ratio
  • Speaker identification
  • Validation

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