  This letter presents an incremental text-to-speech (TTS) method that performs
synthesis in small linguistic units while maintaining the naturalness of output
speech. Incremental TTS is generally subject to a trade-off between latency and
synthetic speech quality. It is challenging to produce high-quality speech with
a low-latency setup that does not make much use of an unobserved future
sentence (hereafter, "lookahead"). To resolve this issue, we propose an
incremental TTS method that uses a pseudo lookahead generated with a language
model to take the future contextual information into account without increasing
latency. Our method can be regarded as imitating a human's incremental reading
and uses pretrained GPT2, which accounts for the large-scale linguistic
knowledge, for the lookahead generation. Evaluation results show that our
method 1) achieves higher speech quality than the method taking only observed
information into account and 2) achieves a speech quality equivalent to waiting
for the future context observation.
