  We propose noise-robust voice conversion (VC) which takes into account the
recording quality and environment of noisy source speech. Conventional
denoising training improves the noise robustness of a VC model by learning
noisy-to-clean VC process. However, the naturalness of the converted speech is
limited when the noise of the source speech is unseen during the training. To
this end, our proposed training conditions a VC model on two latent variables
representing the recording quality and environment of the source speech. These
latent variables are derived from deep neural networks pre-trained on recording
quality assessment and acoustic scene classification and calculated in an
utterance-wise or frame-wise manner. As a result, the trained VC model can
explicitly learn information about speech degradation during the training.
Objective and subjective evaluations show that our training improves the
quality of the converted speech compared to the conventional training.
