Deploying a Speech Recognition Model for Under-Resourced Languages: A Case Study on Dioula Wake Words 1, 2, 3, and 4-Reference-Cited by-同舟云学术

Deploying a Speech Recognition Model for Under-Resourced Languages: A Case Study on Dioula Wake Words 1, 2, 3, and 4

Published:2023-12-15 Issue: Volume:201 Page:111-118
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval
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Author:

Ouedraogo Ismaila¹^ORCID,Some Borlli Michel Jonas²^ORCID,Keita Zakaria Cheick Oumar²^ORCID,Nabaloum Emile²^ORCID,Bationo Fabrice²^ORCID,Benedikter Roland³^ORCID,Diallo Gayo⁴^ORCID

Affiliation:

1. Team AHeaD Inserm 1219, University of Bordeaux, France

2. School of informatics, University of Nazi Boni, Burkina Faso

3. Center for Advanced Studies, Eurac Research, Italy

4. Team AHeaD, Inserm 1219, University of Bordeaux, France

Funder

Agence Universitaire de la Francophonie

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3639233.3639345

Reference19 articles.

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2. [2] Babirye C, Nakatumba-Nabende J, Katumba A, Ogwang R, Francis JT, Mukiibi J, et al. Building text and speech datasets for low resourced languages: A case of languages in East Africa 2022.

3. [3] Krauwer S. The basic language resource kit (BLARK) as the first milestone for the language resources roadmap. vol. 2003, 2003, p. 15.

4. [5] Omniglot. Dioula language 2021. — https://omniglot.com/writing/dioula.htm (accessed September 25, 2023).

5. [6] Mangeot M, Sadat F. TALN-RECITAL 2014 Workshop TALAf 2014: Traitement Automatique des Langues Africaines (TALAf 2014: African Language Processing), 2014.