Basu, Nabanita, Weber, Philip, Bali, Agnes S, Rosas-Aguilar, Claudia, Edmond, Gary, Martire, Kristy A and Morrison, Geoffrey Stewart (2023). Speaker identification in courtroom contexts – Part II: Investigation of bias in individual listeners’ responses. Forensic Science International, 349 ,
Abstract
In “Speaker identification in courtroom contexts – Part I” individual listeners made speaker-identification judgements on pairs of recordings which reflected the conditions of the questioned-speaker and known-speaker recordings in a real case. The recording conditions were poor, and there was a mismatch between the questioned-speaker condition and the known-speaker condition. No contextual information that could potentially bias listeners’ responses was included in the experiment condition – it was decontextualized with respect to case circumstances and with respect to other evidence that could be presented in the context of a case. Listeners’ responses exhibited a bias in favour of the different-speaker hypothesis. It was hypothesized that the bias was due to the poor and mismatched recording conditions. The present research compares speaker-identification performance between: (1) listeners under the original Part I experiment condition, (2) listeners who were informed ahead of time that the recording conditions would make the recordings sound more different from one another than had they both been high-quality recordings, and (3) listeners who were presented with high-quality versions of the recordings. Under all experiment conditions, there was a substantial bias in favour of the different-speaker hypothesis. The bias in favour of the different-speaker hypothesis therefore appears not to be due to the poor and mismatched recording conditions.
Publication DOI: | https://doi.org/10.1016/j.forsciint.2023.111768 |
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Divisions: | ?? 50811700Jl ?? College of Engineering & Physical Sciences College of Business and Social Sciences > Aston Institute for Forensic Linguistics College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application Aston University (General) |
Additional Information: | Funding Information: This research was supported by Research England’s Expanding Excellence in England Fund as part of funding for the Aston Institute for Forensic Linguistics 2019–2024. Publisher Copyright: Copyright © 2023 The Author(s). Published by Elsevier B.V. This article is licensed under a Creative commons Attribution 4.0 International (CC BY 4.0) |
Uncontrolled Keywords: | Bias,Forensic voice comparison,Likelihood ratio,Recording condition,Speaker identification |
Publication ISSN: | 0379-0738 |
Last Modified: | 02 Dec 2024 08:56 |
Date Deposited: | 22 Jun 2023 15:29 |
Full Text Link: | |
Related URLs: |
https://www.sci ... 379073823002189
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2023-08 |
Published Online Date: | 2023-06-22 |
Accepted Date: | 2023-06-20 |
Authors: |
Basu, Nabanita
Weber, Philip ( 0000-0002-3121-9625) Bali, Agnes S Rosas-Aguilar, Claudia Edmond, Gary Martire, Kristy A Morrison, Geoffrey Stewart ( 0000-0001-8608-8207) |
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