  This paper investigates how to leverage a DurIAN-based average model to
enable a new speaker to have both accurate pronunciation and fluent
cross-lingual speaking with very limited monolingual data. A weakness of the
recently proposed end-to-end text-to-speech (TTS) systems is that robust
alignment is hard to achieve, which hinders it to scale well with very limited
data. To cope with this issue, we introduce AdaDurIAN by training an improved
DurIAN-based average model and leverage it to few-shot learning with the shared
speaker-independent content encoder across different speakers. Several few-shot
learning tasks in our experiments show AdaDurIAN can outperform the baseline
end-to-end system by a large margin. Subjective evaluations also show that
AdaDurIAN yields higher mean opinion score (MOS) of naturalness and more
preferences of speaker similarity. In addition, we also apply AdaDurIAN to
emotion transfer tasks and demonstrate its promising performance.
