# The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints

arXiv: https://arxiv.org/abs/2608.07980 · HTML: https://mlsanity.com/paper/2608.07980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 217 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 30 of 38)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Exploring wav2vec 2.0 on speaker verification and language identification](https://mlsanity.com/paper/2012.06185/) (2020)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [The IDLAB VoxSRC-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verification](https://mlsanity.com/paper/2010.11255/) (2020)
- [Warning: Humans Cannot Reliably Detect Speech Deepfakes](https://mlsanity.com/paper/2301.07829/) (2023)
- [SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention](https://mlsanity.com/paper/2312.08676/) (2023)
- [Probing the Information Encoded in X-vectors](https://mlsanity.com/paper/1909.06351/) (2019)
- [ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control](https://mlsanity.com/paper/2406.01205/) (2024)
- [Selective Listening by Synchronizing Speech with Lips](https://mlsanity.com/paper/2106.07150/) (2021)
- [VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/1912.02522/) (2019)
- [People are poorly equipped to detect AI-powered voice clones](https://mlsanity.com/paper/2410.03791/) (2024)
- [USEV: Universal Speaker Extraction with Visual Cue](https://mlsanity.com/paper/2109.14831/) (2021)
- [The voice of COVID-19: Acoustic correlates of infection](https://mlsanity.com/paper/2012.09478/) (2020)
- [To train or not to train adversarially: A study of bias mitigation strategies for speaker recognition](https://mlsanity.com/paper/2203.09122/) (2022)
- [Forensic deepfake audio detection using segmental speech features](https://mlsanity.com/paper/2505.13847/) (2025)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [MeanVC: Lightweight and Streaming Zero-Shot Voice Conversion via Mean Flows](https://mlsanity.com/paper/2510.08392/) (2025)
- [Analyzing speaker verification embedding extractors and back-ends under language and channel mismatch](https://mlsanity.com/paper/2203.10300/) (2022)
- [Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection](https://mlsanity.com/paper/2507.08626/) (2025)
- [Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?](https://mlsanity.com/paper/2204.03965/) (2022)
- [Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion](https://mlsanity.com/paper/2302.12434/) (2023)
- [Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech](https://mlsanity.com/paper/2306.07501/) (2023)
- [Utilizing Speaker Profiles for Impersonation Audio Detection](https://mlsanity.com/paper/2408.17009/) (2024)
- [Classification of Spontaneous and Scripted Speech for Multilingual Audio](https://mlsanity.com/paper/2412.11896/) (2024)
- [PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake Detection and Naturalness Evaluation](https://mlsanity.com/paper/2603.15037/) (2026)
- [Speaker discrimination in humans and machines: Effects of speaking style variability](https://mlsanity.com/paper/2008.03617/) (2020)
- [Compact Speaker Embedding: lrx-vector](https://mlsanity.com/paper/2008.05011/) (2020)
- [Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation](https://mlsanity.com/paper/2603.21078/) (2026)

## Abstract (arXiv)

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. We argue that speaker identity assessment does not require, and current evidence does not support, the existence of a stable and individually unique voiceprint. For speaker recognition and voice biometrics, this distinction motivates interpreting learned speaker representations with respect to the conditions under which they are trained and evaluated, and explicitly assessing their robustness to relevant sources of within-speaker variability, domain mismatch, and synthetic manipulation.