A Computational Model for Interactive Transcription

William Lane, Mat Bettinson, Steven Bird


Abstract
Transcribing low resource languages can be challenging in the absence of a good lexicon and trained transcribers. Accordingly, we seek a way to enable interactive transcription whereby the machine amplifies human efforts. This paper presents a data model and a system architecture for interactive transcription, supporting multiple modes of interactivity, increasing the likelihood of finding tasks that engage local participation in language work. The approach also supports other applications which are useful in our context, including spoken document retrieval and language learning.
Anthology ID:
2021.dash-1.16
Volume:
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances
Month:
June
Year:
2021
Address:
Online
Editors:
Eduard Dragut, Yunyao Li, Lucian Popa, Slobodan Vucetic
Venue:
DaSH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
105–111
Language:
URL:
https://aclanthology.org/2021.dash-1.16
DOI:
10.18653/v1/2021.dash-1.16
Bibkey:
Cite (ACL):
William Lane, Mat Bettinson, and Steven Bird. 2021. A Computational Model for Interactive Transcription. In Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, pages 105–111, Online. Association for Computational Linguistics.
Cite (Informal):
A Computational Model for Interactive Transcription (Lane et al., DaSH 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.dash-1.16.pdf