People are poorly equipped to detect AI-powered voice clones

Sarah Barrington, Emily A. Cooper, Hany Farid
10/3/2024
cs.HCcs.AIcs.CY

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Known-Voice Cloning and the Limits of Personality Transfer for Character Design

84% match2026

Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features

84% match1 repo402023

Voice "Cloning" is Style Transfer

83% match2026

"It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services

81% match2025

Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion

81% match2023

MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora

80% match2026

Cite this paper

@article{barrington2024people,
  title  = {People are poorly equipped to detect AI-powered voice clones},
  author = {Sarah Barrington and Emily A. Cooper and Hany Farid},
  year   = {2024},
  eprint = {2410.03791},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.03791v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2410.03791.svg)](https://codeofpaper.com/paper/2410.03791)

Discussion