  The prosody of a spoken word is determined by its surrounding context. In
incremental text-to-speech synthesis, where the synthesizer produces an output
before it has access to the complete input, the full context is often unknown
which can result in a loss of naturalness in the synthesized speech. In this
paper, we investigate whether the use of predicted future text can attenuate
this loss. We compare several test conditions of next future word: (a) unknown
(zero-word), (b) language model predicted, (c) randomly predicted and (d)
ground-truth. We measure the prosodic features (pitch, energy and duration) and
find that predicted text provides significant improvements over a zero-word
lookahead, but only slight gains over random-word lookahead. We confirm these
results with a perceptive test.
