  The information bottleneck auto-encoder is a tool for disentanglement
commonly used for voice transformation. The successful disentanglement relies
on the right choice of bottleneck size. Previous bottleneck auto-encoders
created the bottleneck by the dimension of the latent space or through vector
quantization and had no means to change the bottleneck size of a specific
model. As the bottleneck removes information from the disentangled
representation, the choice of bottleneck size is a trade-off between
disentanglement and synthesis quality. We propose to build the information
bottleneck using dropout which allows us to change the bottleneck through the
dropout rate and investigate adapting the bottleneck size depending on the
context. We experimentally explore into using the adaptive bottleneck for pitch
transformation and demonstrate that the adaptive bottleneck leads to improved
disentanglement of the F0 parameter for both, speech and singing voice leading
to improved synthesis quality. Using the variable bottleneck size, we were able
to achieve disentanglement for singing voice including extremely high pitches
and create a universal voice model, that works on both speech and singing voice
with improved synthesis quality.

Audio and Speech Processing;Sound