OnomaCap: Making Non-speech Sound Captions Accessible and Enjoyable through Onomatopoeic Sound Representation

Abstract

Non-speech sounds play an important role in setting the mood of a video and aiding comprehension. However, current non-speech sound captioning practices focus primarily on sound categories, which fails to provide a rich sound experience for d/Deaf and hard-of-hearing (DHH) viewers. Onomatopoeia, which succinctly captures expressive sound information, offers a potential solution but remains underutilized in non-speech sound captioning. This paper investigates how onomatopoeia benefits DHH audiences in non-speech sound captioning. We collected 7,962 sound-onomatopoeia pairs from listeners and developed a sound-onomatopoeia model that automatically transcribes sounds into onomatopoeic descriptions indistinguishable from human-generated ones. A user evaluation of 25 DHH participants using the model-generated onomatopoeia demonstrated that onomatopoeia significantly improved their video viewing experience. Participants most favored captions with onomatopoeia and category, and expressed a desire to see such captions across genres. We discuss the benefits and challenges of using onomatopoeia in non-speech sound captions, offering insights for future practices. © 2025 Copyright held by the owner/author(s).