  Data efficient voice cloning aims at synthesizing target speaker's voice with
only a few enrollment samples at hand. To this end, speaker adaptation and
speaker encoding are two typical methods based on base model trained from
multiple speakers. The former uses a small set of target speaker data to
transfer the multi-speaker model to target speaker's voice through direct model
update, while in the latter, only a few seconds of target speaker's audio
directly goes through an extra speaker encoding model along with the
multi-speaker model to synthesize target speaker's voice without model update.
Nevertheless, the two methods need clean target speaker data. However, the
samples provided by user may inevitably contain acoustic noise in real
applications. It's still challenging to generating target voice with noisy
data. In this paper, we study the data efficient voice cloning problem from
noisy samples under the sequence-to-sequence based TTS paradigm. Specifically,
we introduce domain adversarial training (DAT) to speaker adaptation and
speaker encoding, which aims to disentangle noise from speech-noise mixture.
Experiments show that for both speaker adaptation and encoding, the proposed
approaches can consistently synthesize clean speech from noisy speaker samples,
apparently outperforming the method adopting state-of-the-art speech
enhancement module.
