English

Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations

Sound 2022-06-28 v1 Computation and Language Audio and Speech Processing

Abstract

We formulated non-speech vocalization (NSV) modeling as a text-to-speech task and verified its viability. Specifically, we evaluated the phonetic expressivity of HUBERT speech units on NSVs and verified our model's ability to control over speaker timbre even though the training data is speaker few-shot. In addition, we substantiated that the heterogeneity in recording conditions is the major obstacle for NSV modeling. Finally, we discussed five improvements over our method for future research. Audio samples of synthesized NSVs are available on our demo page: https://resemble-ai.github.io/reLaugh.

Keywords

Cite

@article{arxiv.2206.12662,
  title  = {Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations},
  author = {Chin-Cheng Hsu},
  journal= {arXiv preprint arXiv:2206.12662},
  year   = {2022}
}
R2 v1 2026-06-24T12:03:53.906Z