A Finite State Aproach to Interactive Transcription

William Lane; Steven Bird

A Finite State Aproach to Interactive Transcription

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

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Abstract

We describe a novel approach to transcribing morphologically complex, local, oral languages. The approach connects with local motivations for participating in language work which center on language learning, accessing the content of audio collections, and applying this knowledge in language revitalization and maintenance. We develop a constraint-based approach to interactive word completion, expressed using Optimality Theoretic constraints, implemented in a finite state transducer, and applied to an Indigenous language. We show that this approach suggests correct full word predictions on 57.9% of the test utterances, and correct partial word predictions on 67.5% of the test utterances. In total, 87% of the test utterances receive full or partial word suggestions which serve to guide the interactive transcription process.

Original language	English
Title of host publication	Proceedings of the first workshop on NLP applications to field linguistics
Place of Publication	Gyeongju
Publisher	International Conference on Computational Linguistics
Pages	1-10
Number of pages	10
Volume	29
Publication status	Published - 1 Oct 2022
Event	The 29th International Conference on Computational Linguistics - Gyeongju, Korea, Republic of Duration: 12 Oct 2022 → 17 Oct 2022 https://coling2022.org/

Conference

Conference	The 29th International Conference on Computational Linguistics
Abbreviated title	COLING
Country/Territory	Korea, Republic of
City	Gyeongju
Period	12/10/22 → 17/10/22
Internet address	https://coling2022.org/

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69441781-oaFinal published version, 1.18 MBLicence: CC BY

https://aclanthology.org/2022.fieldmatters-1.1Licence: CC BY

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@inproceedings{dabdd420638a4edd9ff8b4d590de989b,

title = "A Finite State Aproach to Interactive Transcription",

abstract = "We describe a novel approach to transcribing morphologically complex, local, oral languages. The approach connects with local motivations for participating in language work which center on language learning, accessing the content of audio collections, and applying this knowledge in language revitalization and maintenance. We develop a constraint-based approach to interactive word completion, expressed using Optimality Theoretic constraints, implemented in a finite state transducer, and applied to an Indigenous language. We show that this approach suggests correct full word predictions on 57.9% of the test utterances, and correct partial word predictions on 67.5% of the test utterances. In total, 87% of the test utterances receive full or partial word suggestions which serve to guide the interactive transcription process.",

author = "William Lane and Steven Bird",

year = "2022",

month = oct,

day = "1",

language = "English",

volume = "29",

pages = "1--10",

booktitle = "Proceedings of the first workshop on NLP applications to field linguistics",

publisher = "International Conference on Computational Linguistics",

note = "The 29th International Conference on<br/>Computational Linguistics, COLING ; Conference date: 12-10-2022 Through 17-10-2022",

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Lane, W & Bird, S 2022, A Finite State Aproach to Interactive Transcription. in Proceedings of the first workshop on NLP applications to field linguistics. vol. 29, International Conference on Computational Linguistics, Gyeongju, pp. 1-10, The 29th International Conference on
Computational Linguistics, Gyeongju, Korea, Republic of, 12/10/22. <https://aclanthology.org/2022.fieldmatters-1.1>

A Finite State Aproach to Interactive Transcription. / Lane, William; Bird, Steven.
Proceedings of the first workshop on NLP applications to field linguistics. Vol. 29 Gyeongju: International Conference on Computational Linguistics, 2022. p. 1-10.

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

TY - GEN

T1 - A Finite State Aproach to Interactive Transcription

AU - Lane, William

AU - Bird, Steven

PY - 2022/10/1

Y1 - 2022/10/1

N2 - We describe a novel approach to transcribing morphologically complex, local, oral languages. The approach connects with local motivations for participating in language work which center on language learning, accessing the content of audio collections, and applying this knowledge in language revitalization and maintenance. We develop a constraint-based approach to interactive word completion, expressed using Optimality Theoretic constraints, implemented in a finite state transducer, and applied to an Indigenous language. We show that this approach suggests correct full word predictions on 57.9% of the test utterances, and correct partial word predictions on 67.5% of the test utterances. In total, 87% of the test utterances receive full or partial word suggestions which serve to guide the interactive transcription process.

AB - We describe a novel approach to transcribing morphologically complex, local, oral languages. The approach connects with local motivations for participating in language work which center on language learning, accessing the content of audio collections, and applying this knowledge in language revitalization and maintenance. We develop a constraint-based approach to interactive word completion, expressed using Optimality Theoretic constraints, implemented in a finite state transducer, and applied to an Indigenous language. We show that this approach suggests correct full word predictions on 57.9% of the test utterances, and correct partial word predictions on 67.5% of the test utterances. In total, 87% of the test utterances receive full or partial word suggestions which serve to guide the interactive transcription process.

M3 - Conference Paper published in Proceedings

VL - 29

SP - 1

EP - 10

BT - Proceedings of the first workshop on NLP applications to field linguistics

PB - International Conference on Computational Linguistics

CY - Gyeongju

T2 - The 29th International Conference on<br/>Computational Linguistics

Y2 - 12 October 2022 through 17 October 2022

ER -

A Finite State Aproach to Interactive Transcription

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