# LaughNet: synthesizing laughter utterances from waveform silhouettes and a single laughter example

arXiv: https://arxiv.org/abs/2110.04946 · HTML: https://mlsanity.com/paper/2110.04946/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [High Fidelity Speech Synthesis with Adversarial Networks](https://mlsanity.com/paper/1909.11646/) (2019)
- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [Supervised and Unsupervised Approaches for Controlling Narrow Lexical Focus in Sequence-to-Sequence Speech Synthesis](https://mlsanity.com/paper/2101.09940/) (2021)
- [Uncovering Latent Style Factors for Expressive Speech Synthesis](https://mlsanity.com/paper/1711.00520/) (2017)
- [Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning](https://mlsanity.com/paper/2008.09483/) (2020)

## Built on by (most cited first, 1 of 1)

- [Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like](https://mlsanity.com/paper/2402.07383/) (2024)

## Abstract (arXiv)

Emotional and controllable speech synthesis is a topic that has received much attention. However, most studies focused on improving the expressiveness and controllability in the context of linguistic content, even though natural verbal human communication is inseparable from spontaneous non-speech expressions such as laughter, crying, or grunting. We propose a model called LaughNet for synthesizing laughter by using waveform silhouettes as inputs. The motivation is not simply synthesizing new laughter utterances, but testing a novel synthesis-control paradigm that uses an abstract representation of the waveform. We conducted basic listening test experiments, and the results showed that LaughNet can synthesize laughter utterances with moderate quality and retain the characteristics of the training example. More importantly, the generated waveforms have shapes similar to the input silhouettes. For future work, we will test the same method on other types of human nonverbal expressions and integrate it into more elaborated synthesis systems.