  Recently, few-shot voice cloning has achieved a significant improvement.
However, most models for few-shot voice cloning are single-modal, and
multi-modal few-shot voice cloning has been understudied. In this paper, we
propose to use multi-modal learning to improve the few-shot voice cloning
performance. Inspired by the recent works on unsupervised speech
representation, the proposed multi-modal system is built by extending Tacotron2
with an unsupervised speech representation module. We evaluate our proposed
system in two few-shot voice cloning scenarios, namely few-shot
text-to-speech(TTS) and voice conversion(VC). Experimental results demonstrate
that the proposed multi-modal learning can significantly improve the few-shot
voice cloning performance over their counterpart single-modal systems.
