  This paper presents a new voice conversion (VC) framework capable of dealing
with both additive noise and reverberation, and its performance evaluation.
There have been studied some VC researches focusing on real-world circumstances
where speech data are interfered with background noise and reverberation. To
deal with more practical conditions where no clean target dataset is available,
one possible approach is zero-shot VC, but its performance tends to degrade
compared with VC using sufficient amount of target speech data. To leverage
large amount of noisy-reverberant target speech data, we propose a three-stage
VC framework based on denoising process using a pretrained denoising model,
dereverberation process using a dereverberation model, and VC process using a
nonparallel VC model based on a variational autoencoder. The experimental
results show that 1) noise and reverberation additively cause significant VC
performance degradation, 2) the proposed method alleviates the adverse effects
caused by both noise and reverberation, and significantly outperforms the
baseline directly trained on the noisy-reverberant speech data, and 3) the
potential degradation introduced by the denoising and dereverberation still
causes noticeable adverse effects on VC performance.
