  In incremental text to speech synthesis (iTTS), the synthesizer produces an
audio output before it has access to the entire input sentence. In this paper,
we study the behavior of a neural sequence-to-sequence TTS system when used in
an incremental mode, i.e. when generating speech output for token n, the system
has access to n + k tokens from the text sequence. We first analyze the impact
of this incremental policy on the evolution of the encoder representations of
token n for different values of k (the lookahead parameter). The results show
that, on average, tokens travel 88% of the way to their full context
representation with a one-word lookahead and 94% after 2 words. We then
investigate which text features are the most influential on the evolution
towards the final representation using a random forest analysis. The results
show that the most salient factors are related to token length. We finally
evaluate the effects of lookahead k at the decoder level, using a MUSHRA
listening test. This test shows results that contrast with the above high
figures: speech synthesis quality obtained with 2 word-lookahead is
significantly lower than the one obtained with the full sentence.
