# People are poorly equipped to detect AI-powered voice clones

arXiv: https://arxiv.org/abs/2410.03791 · HTML: https://mlsanity.com/paper/2410.03791/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [Warning: Humans Cannot Reliably Detect Speech Deepfakes](https://mlsanity.com/paper/2301.07829/) (2023)
- [Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video](https://mlsanity.com/paper/2202.12883/) (2022)
- [Human Perception of Audio Deepfakes](https://mlsanity.com/paper/2107.09667/) (2021)
- [Testing Human Ability To Detect Deepfake Images of Human Faces](https://mlsanity.com/paper/2212.05056/) (2022)
- [Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features](https://mlsanity.com/paper/2307.07683/) (2023)

## Built on by (most cited first, 13 of 13)

- [Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images](https://mlsanity.com/paper/2502.11989/) (2025)
- ["It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services](https://mlsanity.com/paper/2504.09346/) (2025)
- [TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks](https://mlsanity.com/paper/2504.20658/) (2025)
- [Human Action CLIPs: Detecting AI-generated Human Motion](https://mlsanity.com/paper/2412.00526/) (2024)
- [Distinguishing Task-Specific and General-Purpose AI in Regulation](https://mlsanity.com/paper/2506.17347/) (2025)
- [Curved Worlds, Clear Boundaries: Generalizing Speech Deepfake Detection using Hyperbolic and Spherical Geometry Spaces](https://mlsanity.com/paper/2511.10793/) (2025)
- [XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection](https://mlsanity.com/paper/2601.02944/) (2026)
- [RealityTest: How People Probe AI Identity and Whether Models Disclose It](https://mlsanity.com/paper/2606.00168/) (2026)
- [CAREBench: A Child-Safety Risk Benchmark for Language Models](https://mlsanity.com/paper/2606.29685/) (2026)
- [Speech Generation Speaker Poisoning: Capability Erasure in Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2603.07551/) (2026)
- [The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints](https://mlsanity.com/paper/2608.07980/) (2026)
- [A survey of AI-generated voices and their detection](https://mlsanity.com/paper/2608.15411/) (2026)
- [Known-Voice Cloning and the Limits of Personality Transfer for Character Design](https://mlsanity.com/paper/2610.18669/) (2026)

## Abstract (arXiv)

As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot consistently identify recordings of AI-generated voices. Specifically, participants perceived the identity of an AI-voice to be the same as its real counterpart approximately 80% of the time, and correctly identified a voice as AI generated only about 60% of the time.