A computational model for interactive transcription

William Abbott Lane; Mat Bettinson; Steven Bird

A computational model for interactive transcription

William Abbott Lane, Mat Bettinson, Steven Bird

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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.

Original language	English
Title of host publication	Proceedings of the Second Workshop on Data Science with Human-in-the-Loop
Subtitle of host publication	Language Advances
Editors	Eduard Dragut, Yunyao Li, Lucian Popa, Slobodan Vucetic
Place of Publication	USA
Publisher	Association for Computational Linguistics (ACL)
Pages	105-111
Number of pages	7
Edition	1
ISBN (Print)	9781954085398
Publication status	Published - Jun 2021
Event	The 2nd Workshop on Data Science with Human-in-the-loop: Language Advances - Virtual Duration: 11 Jun 2021 → 11 Jun 2021

Workshop

Workshop	The 2nd Workshop on Data Science with Human-in-the-loop
Abbreviated title	DaSH-LA 2021
City	Virtual
Period	11/06/21 → 11/06/21

Access to Document

46554141-oaFinal published version, 1.09 MBLicence: CC BY

https://aclanthology.org/2021.dash-1.0/

Cite this

@inproceedings{c1f258ea57c64809b48b8e3306ddb6ff,

title = "A computational model for interactive transcription",

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.",

author = "Lane, {William Abbott} and Mat Bettinson and Steven Bird",

year = "2021",

month = jun,

language = "English",

isbn = "9781954085398",

pages = "105--111",

editor = "Eduard Dragut and Yunyao Li and Lucian Popa and Slobodan Vucetic",

booktitle = "Proceedings of the Second Workshop on Data Science with Human-in-the-Loop",

publisher = "Association for Computational Linguistics (ACL)",

edition = "1",

note = "The 2nd Workshop on Data Science with Human-in-the-loop : Language Advances, DaSH-LA 2021 ; Conference date: 11-06-2021 Through 11-06-2021",

}

Lane, WA , Bettinson, M & Bird, S 2021, A computational model for interactive transcription. in E Dragut, Y Li, L Popa & S Vucetic (eds), Proceedings of the Second Workshop on Data Science with Human-in-the-Loop: Language Advances. 1 edn, Association for Computational Linguistics (ACL), USA, pp. 105-111, The 2nd Workshop on Data Science with Human-in-the-loop, Virtual, 11/06/21.

A computational model for interactive transcription. / Lane, William Abbott ; Bettinson, Mat ; Bird, Steven.

Proceedings of the Second Workshop on Data Science with Human-in-the-Loop: Language Advances. ed. / Eduard Dragut; Yunyao Li; Lucian Popa; Slobodan Vucetic. 1. ed. USA : Association for Computational Linguistics (ACL), 2021. p. 105-111.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

TY - GEN

T1 - A computational model for interactive transcription

AU - Lane, William Abbott

AU - Bettinson, Mat

AU - Bird, Steven

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

UR - https://aclanthology.org/2021.dash-1.0/

M3 - Conference Paper published in Proceedings

SN - 9781954085398

SP - 105

EP - 111

BT - Proceedings of the Second Workshop on Data Science with Human-in-the-Loop

A2 - Dragut, Eduard

A2 - Li, Yunyao

A2 - Popa, Lucian

A2 - Vucetic, Slobodan

PB - Association for Computational Linguistics (ACL)

CY - USA

T2 - The 2nd Workshop on Data Science with Human-in-the-loop

Y2 - 11 June 2021 through 11 June 2021

ER -

A computational model for interactive transcription

Abstract

Workshop

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