  The success of automatic speaker verification shows that discriminative
speaker representations can be extracted from neutral speech. However, as a
kind of non-verbal voice, laughter should also carry speaker information
intuitively. Thus, this paper focuses on exploring speaker verification about
utterances containing non-verbal laughter segments. We collect a set of clips
with laughter components by conducting a laughter detection script on VoxCeleb
and part of the CN-Celeb dataset. To further filter untrusted clips,
probability scores are calculated by our binary laughter detection classifier,
which is pre-trained by pure laughter and neutral speech. After that, based on
the clips whose scores are over the threshold, we construct trials under two
different evaluation scenarios: Laughter-Laughter (LL) and Speech-Laughter
(SL). Then a novel method called Laughter-Splicing based Network (LSN) is
proposed, which can significantly boost performance in both scenarios and
maintain the performance on the neutral speech, such as the VoxCeleb1 test set.
Specifically, our system achieves relative 20% and 22% improvement on
Laughter-Laughter and Speech-Laughter trials, respectively. The meta-data and
sample clips have been released at https://github.com/nevermoreLin/Laugh_LSN.
