  We present a large-scale in-the-wild Japanese laughter corpus and a laughter
synthesis method. Previous work on laughter synthesis lacks not only data but
also proper ways to represent laughter. To solve these problems, we first
propose an in-the-wild corpus comprising $3.5$ hours of laughter, which is to
our best knowledge the largest laughter corpus designed for laughter synthesis.
We then propose pseudo phonetic tokens (PPTs) to represent laughter by a
sequence of discrete tokens, which are obtained by training a clustering model
on features extracted from laughter by a pretrained self-supervised model.
Laughter can then be synthesized by feeding PPTs into a text-to-speech system.
We further show PPTs can be used to train a language model for unconditional
laughter generation. Results of comprehensive subjective and objective
evaluations demonstrate that the proposed method significantly outperforms a
baseline method, and can generate natural laughter unconditionally.

Sound;Audio and Speech Processing