MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method
Laughter is a social non-vocalization that is universal across cultures and languages, and is crucial for human communication, including social bonding and communication signaling. However, detecting laughter in audio is a challenging task, and segmenting is even more difficult. Currently, Machine Learning methods generally rely on costly manual annotation, and their datasets are mostly based on English contexts. Thus, we propose an unsupervised multilingual method that sets up the laughter segmentation task as an anomaly detection of energy-based segmented audio sequences. Our method applies an Isolation Forest on audio representations learned from BYOL-A encoder. We compare our method w...
| Authors | Sofia Callejas, Nahuel Gomez, Catherine Pelachaud et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 May 2026 |
| Last updated at source | 13 May 2026 |
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