  It is widely acknowledged that discriminative representation for speaker
verification can be extracted from verbal speech. However, how much speaker
information that non-verbal vocalization carries is still a puzzle. This paper
explores speaker verification based on the most ubiquitous form of non-verbal
voice, laughter. First, we use a semi-automatic pipeline to collect a new
Haha-Pod dataset from open-source podcast media. The dataset contains over 240
speakers' laughter clips with corresponding high-quality verbal speech. Second,
we propose a Two-Stage Teacher-Student (2S-TS) framework to minimize the
within-speaker embedding distance between verbal and non-verbal (laughter)
signals. Considering Haha-Pod as a test set, two trials (S2L-Eval) are designed
to verify the speaker's identity through laugh sounds. Experimental results
demonstrate that our method can significantly improve the performance of the
S2L-Eval test set with only a minor degradation on the VoxCeleb1 test set. The
resources for the Haha-Pod dataset can be found at
https://github.com/nevermoreLin/HahaPod.
