  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.
