This paper presents a noise-robust voice conversion method with high-quefrency boosting via sub-band cepstrum conversion and fusion based on the bidirectional long short-term memory (BLSTM) neural networks that can convert parameters of vocal tracks of a source speaker into those of a target speaker.

Experimental results show that Voicy outperforms other tested VC techniques in terms of naturalness and target speaker similarity in noisy reverberant environments. We have validated our approach using a noisy reverberant version of the LibriSpeech dataset. 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. 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. To address this limitation, we propose Voicy, a new VC framework particularly tailored for noisy speech. However, many acoustic environments are noisy and reverberant, severely restricting the applicability of popular VC methods to such scenarios. While there is a rich literature on VC, most proposed methods are trained and evaluated on clean speech recordings. 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.

The voice returns its voicey