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It is widely acknowledged that discriminative representation for speakerverification can be extracted from verbal speech. However, how much speakerinformation that non-verbal vocalization carries is still a puzzle. This paperexplores speaker verification based on the most ubiquitous form of non-verbalvoice, laughter. First, we use a semi-automatic pipeline to collect a newHaha-Pod dataset from open-source podcast media. The dataset contains over 240speakers' laughter clips with corresponding high-quality verbal speech. Second,we propose a Two-Stage Teacher-Student (2S-TS) framework to minimize thewithin-speaker embedding distance between verbal and non-verbal (laughter)signals. Considering Haha-Pod as a test set, two trials (S2L-Eval) are designedto verify the speaker's identity through laugh sounds. Experimental resultsdemonstrate that our method can significantly improve the performance of theS2L-Eval test set with only a minor degradation on the VoxCeleb1 test set. Theresources for the Haha-Pod dataset can be found athttps://github.com/nevermoreLin/HahaPod.

Haha-Pod: An Attempt for Laughter-based Non-Verbal Speaker Verification