# From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories

arXiv: https://arxiv.org/abs/1908.07590 · HTML: https://mlsanity.com/paper/1908.07590/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

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

- [MultiActor-Audiobook: Zero-Shot Audiobook Generation with Faces and Voices of Multiple Speakers](https://mlsanity.com/paper/2505.13082/) (2025)

## Abstract (arXiv)

Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.