Assessing the Admissibility of a New Generation of Forensic Voice Comparison Testimony
80 Pages Posted: 12 Dec 2016
Date Written: December 11, 2016
This article provides a primer on forensic voice comparison (aka forensic speaker recognition), a branch of forensic science in which the forensic practitioner analyzes a voice recording in order to provide an expert opinion that will help the trier-of-fact determine the identity of the speaker. The article begins with an explanation of ways in which human speech varies within and between speakers. It then discusses different technical approaches that forensic practitioners have used to compare voice recordings, and frameworks of reasoning that practitioners have used for evaluating the evidence and reporting its strength. It then discusses procedures for empirical validation of the performance of forensic voice comparison systems. It also discusses the potential influence of contextual bias and ways to reduce this. Building on this scientific foundation, the article then offers analysis, commentary, and recommendations on how courts evaluate the admissibility of forensic voice comparison testimony under the Daubert and Frye standards. It reviews past rulings such as U.S. v. Angleton, 269 F.Supp 2nd 892 (S.D. Tex. 2003) that found expert testimony based on the spectrographic approach inadmissible under Daubert. The article also offers a detailed analysis of the evidence presented in the recent Daubert hearing in U.S. v. Ahmed, et al. 2015 EDNY 12-CR-661, which included testimony based on the newer automatic approach. The scientific testimony proffered in Ahmed is used to illustrate the issues courts are likely to face when considering the admissibility of forensic voice comparison testimony in the future. The article concludes with a discussion of how proponents of forensic voice comparison testimony might meet a reasonably rigorous application of the Daubert standard and thereby ensure that such testimony is sufficiently trustworthy to be used in court.
Keywords: Forensic Voice Comparison, Voiceprint, Automatic Speaker Recognition, Admissibility, Daubert, Frye
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