  In this paper, we describe a statistical parametric speech synthesis approach
with unit-level acoustic representation. In conventional deep neural network
based speech synthesis, the input text features are repeated for the entire
duration of phoneme for mapping text and speech parameters. This mapping is
learnt at the frame-level which is the de-facto acoustic representation.
However much of this computational requirement can be drastically reduced if
every unit can be represented with a fixed-dimensional representation. Using
recurrent neural network based auto-encoder, we show that it is indeed possible
to map units of varying duration to a single vector. We then use this acoustic
representation at unit-level to synthesize speech using deep neural network
based statistical parametric speech synthesis technique. Results show that the
proposed approach is able to synthesize at the same quality as the conventional
frame based approach at a highly reduced computational cost.
