  This paper proposes a new approach to duration modelling for statistical
parametric speech synthesis in which a recurrent statistical model is trained
to output a phone transition probability at each timestep (acoustic frame).
Unlike conventional approaches to duration modelling -- which assume that
duration distributions have a particular form (e.g., a Gaussian) and use the
mean of that distribution for synthesis -- our approach can in principle model
any distribution supported on the non-negative integers. Generation from this
model can be performed in many ways; here we consider output generation based
on the median predicted duration. The median is more typical (more probable)
than the conventional mean duration, is robust to training-data irregularities,
and enables incremental generation. Furthermore, a frame-level approach to
duration prediction is consistent with a longer-term goal of modelling
durations and acoustic features together. Results indicate that the proposed
method is competitive with baseline approaches in approximating the median
duration of held-out natural speech.
