  Despite recent advances in generative modeling for text-to-speech synthesis,
these models do not yet have the same fine-grained adjustability of
pitch-conditioned deterministic models such as FastPitch and FastSpeech2. Pitch
information is not only low-dimensional, but also discontinuous, making it
particularly difficult to model in a generative setting. Our work explores
several techniques for handling the aforementioned issues in the context of
Normalizing Flow models. We also find this problem to be very well suited for
Neural Spline flows, which is a highly expressive alternative to the more
common affine-coupling mechanism in Normalizing Flows.
