# Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments

arXiv: https://arxiv.org/abs/2106.08873 · HTML: https://mlsanity.com/paper/2106.08873/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks](https://mlsanity.com/paper/1806.02169/) (2018)
- [The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods](https://mlsanity.com/paper/1804.04262/) (2018)
- [Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion](https://mlsanity.com/paper/1906.00794/) (2019)
- [High-quality nonparallel voice conversion based on cycle-consistent adversarial network](https://mlsanity.com/paper/1804.00425/) (2018)
- [ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder](https://mlsanity.com/paper/1808.05092/) (2018)
- [Voice Conversion for Whispered Speech Synthesis](https://mlsanity.com/paper/1912.05289/) (2019)

## Built on by (most cited first, 4 of 4)

- [Robust One-Shot Singing Voice Conversion](https://mlsanity.com/paper/2210.11096/) (2022)
- [Toward Degradation-Robust Voice Conversion](https://mlsanity.com/paper/2110.07537/) (2021)
- [HiFi-VC: High Quality ASR-Based Voice Conversion](https://mlsanity.com/paper/2203.16937/) (2022)
- [Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation](https://mlsanity.com/paper/2512.06304/) (2025)

## Abstract (arXiv)

Voice Conversion (VC) is a technique that aims to transform the non-linguistic information of a source utterance to change the perceived identity of the speaker. While there is a rich literature on VC, most proposed methods are trained and evaluated on clean speech recordings. However, many acoustic environments are noisy and reverberant, severely restricting the applicability of popular VC methods to such scenarios. To address this limitation, we propose Voicy, a new VC framework particularly tailored for noisy speech. Our method, which is inspired by the de-noising auto-encoders framework, is comprised of four encoders (speaker, content, phonetic and acoustic-ASR) and one decoder. Importantly, Voicy is capable of performing non-parallel zero-shot VC, an important requirement for any VC system that needs to work on speakers not seen during training. We have validated our approach using a noisy reverberant version of the LibriSpeech dataset. Experimental results show that Voicy outperforms other tested VC techniques in terms of naturalness and target speaker similarity in noisy reverberant environments.