Abstract
An increasing number of papers have been addressing issues related to low-resource languages and the transcription bottleneck paradigm. After several years spent in Northern Australia, where some of the strongest Aboriginal languages are spoken, we could observe a gap between the motivations depicted in research contributions in this space and the Northern Australian context. In this paper, we address this gap in research by exploring the potential of speech recognition in an Aboriginal community. We describe our work from training a spoken term detection system to its implementation in an activity with Aboriginal participants. We report here on one side how speech recognition technologies can find their place in an Aboriginal context and, on the other, methodological paths that allowed us to reach better comprehension and engagement from Aboriginal participants.- Anthology ID:
- 2022.coling-1.376
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4274–4285
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.376
- DOI:
- Cite (ACL):
- Éric Le Ferrand, Steven Bird, and Laurent Besacier. 2022. Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4274–4285, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Fashioning Local Designs from Generic Speech Technologies in an Australian Aboriginal Community (Le Ferrand et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.coling-1.376.pdf