  Voice Conversion (VC) must be achieved while maintaining the content of the
source speech and representing the characteristics of the target speaker. The
existing methods do not simultaneously satisfy the above two aspects of VC, and
their conversion outputs suffer from a trade-off problem between maintaining
source contents and target characteristics. In this study, we propose Triple
Adaptive Attention Normalization VC (TriAAN-VC), comprising an encoder-decoder
and an attention-based adaptive normalization block, that can be applied to
non-parallel any-to-any VC. The proposed adaptive normalization block extracts
target speaker representations and achieves conversion while minimizing the
loss of the source content with siamese loss. We evaluated TriAAN-VC on the
VCTK dataset in terms of the maintenance of the source content and target
speaker similarity. Experimental results for one-shot VC suggest that TriAAN-VC
achieves state-of-the-art performance while mitigating the trade-off problem
encountered in the existing VC methods.
