  Disentangling speaker and content attributes of a speech signal into separate
latent representations followed by decoding the content with an exchanged
speaker representation is a popular approach for voice conversion, which can be
trained with non-parallel and unlabeled speech data. However, previous
approaches perform disentanglement only implicitly via some sort of information
bottleneck or normalization, where it is usually hard to find a good trade-off
between voice conversion and content reconstruction. Further, previous works
usually do not consider an adaptation of the speaking rate to the target
speaker or they put some major restrictions to the data or use case. Therefore,
the contribution of this work is two-fold. First, we employ an explicit and
fully unsupervised disentanglement approach, which has previously only been
used for representation learning, and show that it allows to obtain both
superior voice conversion and content reconstruction. Second, we investigate
simple and generic approaches to linearly adapt the length of a speech signal,
and hence the speaking rate, to a target speaker and show that the proposed
adaptation allows to increase the speaking rate similarity with respect to the
target speaker.

Audio and Speech Processing