# Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning

arXiv: https://arxiv.org/abs/2008.09483 · HTML: https://mlsanity.com/paper/2008.09483/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Towards human-like spoken dialogue generation between AI agents from written dialogue](https://mlsanity.com/paper/2310.01088/) (2023)
- [CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations](https://mlsanity.com/paper/2404.06690/) (2024)
- [Generating Diverse Realistic Laughter for Interactive Art](https://mlsanity.com/paper/2111.03146/) (2021)
- [Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models](https://mlsanity.com/paper/2305.08854/) (2023)
- [LaughNet: synthesizing laughter utterances from waveform silhouettes and a single laughter example](https://mlsanity.com/paper/2110.04946/) (2021)

## Abstract (arXiv)

Despite the growing interest for expressive speech synthesis, synthesis of nonverbal expressions is an under-explored area. In this paper we propose an audio laughter synthesis system based on a sequence-to-sequence TTS synthesis system. We leverage transfer learning by training a deep learning model to learn to generate both speech and laughs from annotations. We evaluate our model with a listening test, comparing its performance to an HMM-based laughter synthesis one and assess that it reaches higher perceived naturalness. Our solution is a first step towards a TTS system that would be able to synthesize speech with a control on amusement level with laughter integration.