🤖 AI Summary
This study critiques the “voiceprint” metaphor, which misleadingly treats the human voice as a stable and unique biometric akin to a fingerprint, and exposes its problematic implications in both technological applications and policy. Integrating insights from phonetics, forensic voice comparison, automatic speaker recognition, deepfake detection, and probabilistic reasoning, the paper systematically examines vocal variability, courtroom practices of voice identification, and challenges in identity verification. It argues against the assumption of a deterministic, fixed vocal signature and advocates instead for a calibrated probabilistic framework that accounts for the dynamic, context-dependent nature of speech. The work calls for a paradigm shift in evaluating voice evidence—one that rigorously incorporates variability, uncertainty, and alternative explanations.
📝 Abstract
In recent years, the term voiceprint has regained attention, particularly in technological applications and policy-making contexts, often carrying the assumption that a person's voice constitutes a stable and unique biometric trace analogous to a fingerprint. Yet this conception has been repeatedly criticized and rejected by forensic voice experts throughout the decades since its introduction. Although voices undoubtedly contain speaker-related information, this simplified conception obscures the highly dynamic and context-dependent nature of speech. This article revisits the voiceprint fallacy and reconsiders what can count as evidence of speaker identity by reviewing the historical development of voiceprint identification, evidence on human voice variability, developments in forensic voice comparison, research on human and automatic speaker recognition, and the recent challenge posed by deepfake speech to speaker identity. We point out that the voiceprint metaphor and its underlying implications are scientifically misleading because they transform a probabilistic source of speaker information into an imagined stable object of identity. To avoid treating voices as imprint-like traces, we recommend that voice evidence be interpreted through validated and calibrated probabilistic frameworks that explicitly account for variability, uncertainty, and alternative explanations.