# Voice Impersonation using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1802.06840 · HTML: https://mlsanity.com/paper/1802.06840/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 17 of 17)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
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- [How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey](https://mlsanity.com/paper/2111.14203/) (2021)
- [DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection](https://mlsanity.com/paper/2304.03323/) (2023)
- [Attributable-Watermarking of Speech Generative Models](https://mlsanity.com/paper/2202.08900/) (2022)
- [A Technological Perspective on Misuse of Available AI](https://mlsanity.com/paper/2403.15325/) (2024)
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- [Attacks and Defenses for Free-Riders in Multi-Discriminator GAN](https://mlsanity.com/paper/2201.09967/) (2022)
- [Faked Speech Detection with Zero Prior Knowledge](https://mlsanity.com/paper/2209.12573/) (2022)
- [Affective social anthropomorphic intelligent system](https://mlsanity.com/paper/2304.11046/) (2023)
- [Provable Speech Attributes Conversion via Latent Independence](https://mlsanity.com/paper/2510.05191/) (2025)
- [IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT](https://mlsanity.com/paper/2103.15073/) (2021)
- [Voice Aging with Audio-Visual Style Transfer](https://mlsanity.com/paper/2110.02411/) (2021)
- [Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks](https://mlsanity.com/paper/2207.14682/) (2022)
- [Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks](https://mlsanity.com/paper/2212.14109/) (2022)
- [Speech to Speech Synthesis for Voice Impersonation](https://mlsanity.com/paper/2602.16721/) (2026)

## Abstract (arXiv)

Voice impersonation is not the same as voice transformation, although the latter is an essential element of it. In voice impersonation, the resultant voice must convincingly convey the impression of having been naturally produced by the target speaker, mimicking not only the pitch and other perceivable signal qualities, but also the style of the target speaker. In this paper, we propose a novel neural network based speech quality- and style- mimicry framework for the synthesis of impersonated voices. The framework is built upon a fast and accurate generative adversarial network model. Given spectrographic representations of source and target speakers' voices, the model learns to mimic the target speaker's voice quality and style, regardless of the linguistic content of either's voice, generating a synthetic spectrogram from which the time domain signal is reconstructed using the Griffin-Lim method. In effect, this model reframes the well-known problem of style-transfer for images as the problem of style-transfer for speech signals, while intrinsically addressing the problem of durational variability of speech sounds. Experiments demonstrate that the model can generate extremely convincing samples of impersonated speech. It is even able to impersonate voices across different genders effectively. Results are qualitatively evaluated using standard procedures for evaluating synthesized voices.