Abstract
Transcribing speech for primarily oral, local languages is often a joint effort involving speakers and outsiders. It is commonly motivated by externally-defined scientific goals, alongside local motivations such as language acquisition and access to heritage materials. We explore the task of ‘learning through transcription’ through the design of a system for collaborative speech annotation. We have developed a prototype to support local and remote learner-speaker interactions in remote Aboriginal communities in northern Australia. We show that situated systems design for inclusive non-expert practice is a promising new direction for working with speakers of local languages.- Anthology ID:
- 2022.computel-1.11
- Volume:
- Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ComputEL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 83–92
- Language:
- URL:
- https://aclanthology.org/2022.computel-1.11
- DOI:
- 10.18653/v1/2022.computel-1.11
- Cite (ACL):
- Mat Bettinson and Steven Bird. 2022. Learning Through Transcription. In Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages, pages 83–92, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Learning Through Transcription (Bettinson & Bird, ComputEL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.computel-1.11.pdf