@inproceedings{chen-etal-2024-cloud,
title = "Cloud-based Platform for Indigenous Language Sound Education",
author = "Chen, Min and
Lee, Chris and
Fish, Naatosi and
Miyashita, Mizuki and
Randall, James",
editor = "Moeller, Sarah and
Agyapong, Godfred and
Arppe, Antti and
Chaudhary, Aditi and
Rijhwani, Shruti and
Cox, Christopher and
Henke, Ryan and
Palmer, Alexis and
Rosenblum, Daisy and
Schwartz, Lane",
booktitle = "Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.computel-1.1",
pages = "1--6",
abstract = "Blackfoot is challenging for English speaking instructors and learners to acquire because it exhibits unique pitch patterns. This study presents MeTILDA (Melodic Transcription in Language Documentation and Application) as a solution to teaching pitch patterns distinct from English. Specifically, we explore ways to improve data visualization through a visualized pronunciation teaching guide called Pitch Art. The working materials can be downloaded or stored in the cloud for further use and collaboration. These features are aimed to facilitate teachers in developing curriculum for learning pronunciation, and provide students with an interactive and integrative learning environment to better understand Blackfoot language and pronunciation.",
}
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<abstract>Blackfoot is challenging for English speaking instructors and learners to acquire because it exhibits unique pitch patterns. This study presents MeTILDA (Melodic Transcription in Language Documentation and Application) as a solution to teaching pitch patterns distinct from English. Specifically, we explore ways to improve data visualization through a visualized pronunciation teaching guide called Pitch Art. The working materials can be downloaded or stored in the cloud for further use and collaboration. These features are aimed to facilitate teachers in developing curriculum for learning pronunciation, and provide students with an interactive and integrative learning environment to better understand Blackfoot language and pronunciation.</abstract>
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%0 Conference Proceedings
%T Cloud-based Platform for Indigenous Language Sound Education
%A Chen, Min
%A Lee, Chris
%A Fish, Naatosi
%A Miyashita, Mizuki
%A Randall, James
%Y Moeller, Sarah
%Y Agyapong, Godfred
%Y Arppe, Antti
%Y Chaudhary, Aditi
%Y Rijhwani, Shruti
%Y Cox, Christopher
%Y Henke, Ryan
%Y Palmer, Alexis
%Y Rosenblum, Daisy
%Y Schwartz, Lane
%S Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F chen-etal-2024-cloud
%X Blackfoot is challenging for English speaking instructors and learners to acquire because it exhibits unique pitch patterns. This study presents MeTILDA (Melodic Transcription in Language Documentation and Application) as a solution to teaching pitch patterns distinct from English. Specifically, we explore ways to improve data visualization through a visualized pronunciation teaching guide called Pitch Art. The working materials can be downloaded or stored in the cloud for further use and collaboration. These features are aimed to facilitate teachers in developing curriculum for learning pronunciation, and provide students with an interactive and integrative learning environment to better understand Blackfoot language and pronunciation.
%U https://aclanthology.org/2024.computel-1.1
%P 1-6
Markdown (Informal)
[Cloud-based Platform for Indigenous Language Sound Education](https://aclanthology.org/2024.computel-1.1) (Chen et al., ComputEL-WS 2024)
ACL
- Min Chen, Chris Lee, Naatosi Fish, Mizuki Miyashita, and James Randall. 2024. Cloud-based Platform for Indigenous Language Sound Education. In Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages, pages 1–6, St. Julians, Malta. Association for Computational Linguistics.