  Text to Speech (TTS) models can generate natural and high-quality speech, but
it is not expressive enough when synthesizing speech with dramatic
expressiveness, such as stand-up comedies. Considering comedians have diverse
personal speech styles, including personal prosody, rhythm, and fillers, it
requires real-world datasets and strong speech style modeling capabilities,
which brings challenges. In this paper, we construct a new dataset and develop
ComedicSpeech, a TTS system tailored for the stand-up comedy synthesis in
low-resource scenarios. First, we extract prosody representation by the prosody
encoder and condition it to the TTS model in a flexible way. Second, we enhance
the personal rhythm modeling by a conditional duration predictor. Third, we
model the personal fillers by introducing comedian-related special tokens.
Experiments show that ComedicSpeech achieves better expressiveness than
baselines with only ten-minute training data for each comedian. The audio
samples are available at https://xh621.github.io/stand-up-comedy-demo/
