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The prosody of a spoken word is determined by its surrounding context. Inincremental text-to-speech synthesis, where the synthesizer produces an outputbefore it has access to the complete input, the full context is often unknownwhich can result in a loss of naturalness in the synthesized speech. In thispaper, we investigate whether the use of predicted future text can attenuatethis 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) andfind that predicted text provides significant improvements over a zero-wordlookahead, but only slight gains over random-word lookahead. We confirm theseresults with a perceptive test.

Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input