# MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method

arXiv: https://arxiv.org/abs/2605.06309 · HTML: https://mlsanity.com/paper/2605.06309/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 14 of 14)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Wav2CLIP: Learning Robust Audio Representations From CLIP](https://mlsanity.com/paper/2110.11499/) (2021)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition](https://mlsanity.com/paper/2205.03433/) (2022)
- [BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations](https://mlsanity.com/paper/2204.07402/) (2022)
- [Face, Body, Voice: Video Person-Clustering with Multiple Modalities](https://mlsanity.com/paper/2105.09939/) (2021)
- [Multi-scale Multi-band DenseNets for Audio Source Separation](https://mlsanity.com/paper/1706.09588/) (2017)
- [FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild](https://mlsanity.com/paper/2401.04210/) (2024)
- [StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos](https://mlsanity.com/paper/2505.18903/) (2025)
- [Laughter Synthesis using Pseudo Phonetic Tokens with a Large-scale In-the-wild Laughter Corpus](https://mlsanity.com/paper/2305.12442/) (2023)
- [A Study of Nationality Bias in Names and Perplexity using Off-the-Shelf Affect-related Tweet Classifiers](https://mlsanity.com/paper/2407.01834/) (2024)
- [A Simple Method to Enhance Pre-trained Language Models with Speech Tokens for Classification](https://mlsanity.com/paper/2512.07571/) (2025)

## Abstract (arXiv)

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 with several state-of-the-art laughter detection algorithms on four datasets, including stand-up comedy, sitcoms, and general short audio from AudioSet. Our results show that state-of-the-art methods are not optimized for multilingual contexts, while our method outperforms them in non-English settings.