RiTTA: Modeling Event Relations in Text-to-Audio Generation
Yuhang He, Yash Jain, Xubo Liu, Andrew Markham, Vibhav Vineet
Abstract
Existing text-to-audio (TTA) generation methods have neither systematically explored audio event relation modeling, nor proposed any new framework to enhance this capability. In this work, we systematically study audio event relation modeling in TTA generation models. We first establish a benchmark for this task by: (1) proposing a comprehensive relation corpus covering all potential relations in real-world scenarios; (2) introducing a new audio event corpus encompassing commonly heard audios; and (3) proposing new evaluation metrics to assess audio event relation modeling from various perspectives. Furthermore, we propose a gated prompt tuning strategy that improves existing TTA models’ relation modeling capability with negligible extra parameters. Specifically, we introduce learnable relation and event prompt that append to the text prompt before feeding to existing TTA models.
BibTeX
@inproceedings{emnlp2025_rittamodelingeve,
title = {RiTTA: Modeling Event Relations in Text-to-Audio Generation},
author = {Yuhang He and Yash Jain and Xubo Liu and Andrew Markham and Vibhav Vineet},
booktitle = {EMNLP 2025},
year = {2025}
}