Short-form verbal arts as a speech data resource in the field

Matthew Faytak; Tianle Yang; Pius W. Akumbu; Ivo Forghema Njuasi; Eric Le Ferrand

doi:10.18653/v1/2026.fieldmatters-1.5

Short-form verbal arts as a speech data resource in the field

Matthew Faytak
, Tianle Yang
, Pius W. Akumbu
, Ivo Forghema Njuasi
, Eric Le Ferrand

Linguistics

SUNY Buffalo
CNRS
University of Buea

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We propose a method for efficient field data collection of speech resource data which leverages short-form verbal arts, namely riddles and proverbs, which permit a predictable transcript to be assigned to naturalistic but conventionalized utterances. As a proof of concept, we describe a 5.25 hour corpus of proverbs and riddles collected for Kom, a low-resource language of Cameroon, and conduct ASR modeling experiments on the corpus. Results suggest that the proposed method yields high quality speech data, albeit with relatively low lexical diversity. We highlight the alignment of the collected data with community priorities for cultural education and preservation in the Cameroonian context.

Original language	English
Title of host publication	Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics
Editors	Eric Le Ferrand, Elena Klyachko, Shu Okabe, Ekaterina Voloshina, Oleg Serikov, Tatiana Shavrina, Ekaterina Vylomova
Publisher	Association for Computational Linguistics (ACL)
Pages	38-45
Number of pages	8
ISBN (Electronic)	9798891763760
DOIs	https://doi.org/10.18653/v1/2026.fieldmatters-1.5
State	Published - 2026
Event	5th Workshop on NLP Applications to Field Linguistics, Field Matters 2026 - Rabat, Morocco Duration: Mar 29 2026 → …

Publication series

Name	Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics

Conference

Conference	5th Workshop on NLP Applications to Field Linguistics, Field Matters 2026
Country/Territory	Morocco
City	Rabat
Period	03/29/26 → …

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10.18653/v1/2026.fieldmatters-1.5

Cite this

Faytak, M., Yang, T., Akumbu, P. W., Njuasi, I. F., & Ferrand, E. L. (2026). Short-form verbal arts as a speech data resource in the field. In E. Le Ferrand, E. Klyachko, S. Okabe, E. Voloshina, O. Serikov, T. Shavrina, & E. Vylomova (Eds.), Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics (pp. 38-45). (Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2026.fieldmatters-1.5

Faytak, Matthew ; Yang, Tianle ; Akumbu, Pius W. et al. / Short-form verbal arts as a speech data resource in the field. Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics. editor / Eric Le Ferrand ; Elena Klyachko ; Shu Okabe ; Ekaterina Voloshina ; Oleg Serikov ; Tatiana Shavrina ; Ekaterina Vylomova. Association for Computational Linguistics (ACL), 2026. pp. 38-45 (Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics).

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title = "Short-form verbal arts as a speech data resource in the field",

abstract = "We propose a method for efficient field data collection of speech resource data which leverages short-form verbal arts, namely riddles and proverbs, which permit a predictable transcript to be assigned to naturalistic but conventionalized utterances. As a proof of concept, we describe a 5.25 hour corpus of proverbs and riddles collected for Kom, a low-resource language of Cameroon, and conduct ASR modeling experiments on the corpus. Results suggest that the proposed method yields high quality speech data, albeit with relatively low lexical diversity. We highlight the alignment of the collected data with community priorities for cultural education and preservation in the Cameroonian context.",

author = "Matthew Faytak and Tianle Yang and Akumbu, \{Pius W.\} and Njuasi, \{Ivo Forghema\} and Ferrand, \{Eric Le\}",

note = "Publisher Copyright: {\textcopyright} 2026 Association for Computational Linguistics. All Rights Reserved.; 5th Workshop on NLP Applications to Field Linguistics, Field Matters 2026 ; Conference date: 29-03-2026",

year = "2026",

doi = "10.18653/v1/2026.fieldmatters-1.5",

language = "English",

series = "Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics",

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

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}

Faytak, M, Yang, T, Akumbu, PW, Njuasi, IF & Ferrand, EL 2026, Short-form verbal arts as a speech data resource in the field. in E Le Ferrand, E Klyachko, S Okabe, E Voloshina, O Serikov, T Shavrina & E Vylomova (eds), Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics. Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics, Association for Computational Linguistics (ACL), pp. 38-45, 5th Workshop on NLP Applications to Field Linguistics, Field Matters 2026, Rabat, Morocco, 03/29/26. https://doi.org/10.18653/v1/2026.fieldmatters-1.5

Short-form verbal arts as a speech data resource in the field. / Faytak, Matthew; Yang, Tianle; Akumbu, Pius W. et al.
Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics. ed. / Eric Le Ferrand; Elena Klyachko; Shu Okabe; Ekaterina Voloshina; Oleg Serikov; Tatiana Shavrina; Ekaterina Vylomova. Association for Computational Linguistics (ACL), 2026. p. 38-45 (Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ferrand, Eric Le

PY - 2026

Y1 - 2026

N2 - We propose a method for efficient field data collection of speech resource data which leverages short-form verbal arts, namely riddles and proverbs, which permit a predictable transcript to be assigned to naturalistic but conventionalized utterances. As a proof of concept, we describe a 5.25 hour corpus of proverbs and riddles collected for Kom, a low-resource language of Cameroon, and conduct ASR modeling experiments on the corpus. Results suggest that the proposed method yields high quality speech data, albeit with relatively low lexical diversity. We highlight the alignment of the collected data with community priorities for cultural education and preservation in the Cameroonian context.

AB - We propose a method for efficient field data collection of speech resource data which leverages short-form verbal arts, namely riddles and proverbs, which permit a predictable transcript to be assigned to naturalistic but conventionalized utterances. As a proof of concept, we describe a 5.25 hour corpus of proverbs and riddles collected for Kom, a low-resource language of Cameroon, and conduct ASR modeling experiments on the corpus. Results suggest that the proposed method yields high quality speech data, albeit with relatively low lexical diversity. We highlight the alignment of the collected data with community priorities for cultural education and preservation in the Cameroonian context.

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Faytak M, Yang T, Akumbu PW, Njuasi IF, Ferrand EL. Short-form verbal arts as a speech data resource in the field. In Le Ferrand E, Klyachko E, Okabe S, Voloshina E, Serikov O, Shavrina T, Vylomova E, editors, Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics. Association for Computational Linguistics (ACL). 2026. p. 38-45. (Field Matters 2026 - Proceedings of the 5th Workshop on NLP Applications to Field Linguistics). doi: 10.18653/v1/2026.fieldmatters-1.5

Short-form verbal arts as a speech data resource in the field

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