  Duration modelling has become an important research problem once more with
the rise of non-attention neural text-to-speech systems. The current approaches
largely fall back to relying on previous statistical parametric speech
synthesis technology for duration prediction, which poorly models the
expressiveness and variability in speech. In this paper, we propose two
alternate approaches to improve duration modelling. First, we propose a
duration model conditioned on phrasing that improves the predicted durations
and provides better modelling of pauses. We show that the duration model
conditioned on phrasing improves the naturalness of speech over our baseline
duration model. Second, we also propose a multi-speaker duration model called
Cauliflow, that uses normalising flows to predict durations that better match
the complex target duration distribution. Cauliflow performs on par with our
other proposed duration model in terms of naturalness, whilst providing
variable durations for the same prompt and variable levels of expressiveness.
Lastly, we propose to condition Cauliflow on parameters that provide an
intuitive control of the pacing and pausing in the synthesised speech in a
novel way.
