@inproceedings{maspong-etal-2024-leveraging,
title = "Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of {M}oklen",
author = "Maspong, Sireemas and
Burroni, Francesco and
Sukanchanon, Teerawee and
Pornpottanamas, Warunsiri and
Pittayaporn, Pittayawat",
editor = "Serikov, Oleg and
Voloshina, Ekaterina and
Postnikova, Anna and
Muradoglu, Saliha and
Le Ferrand, Eric and
Klyachko, Elena and
Vylomova, Ekaterina and
Shavrina, Tatiana and
Tyers, Francis",
booktitle = "Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fieldmatters-1.5",
pages = "37--42",
abstract = "Moklen, a tonal Austronesian language spoken in Thailand, exhibits two tones with unbalanced distributions. We employed machine learning techniques for time-series classification to investigate its acoustic properties. Our analysis reveals that a synergy between pitch and vowel quality is crucial for tone distinction, as the model trained with these features achieved the highest accuracy.",
}
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%0 Conference Proceedings
%T Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen
%A Maspong, Sireemas
%A Burroni, Francesco
%A Sukanchanon, Teerawee
%A Pornpottanamas, Warunsiri
%A Pittayaporn, Pittayawat
%Y Serikov, Oleg
%Y Voloshina, Ekaterina
%Y Postnikova, Anna
%Y Muradoglu, Saliha
%Y Le Ferrand, Eric
%Y Klyachko, Elena
%Y Vylomova, Ekaterina
%Y Shavrina, Tatiana
%Y Tyers, Francis
%S Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F maspong-etal-2024-leveraging
%X Moklen, a tonal Austronesian language spoken in Thailand, exhibits two tones with unbalanced distributions. We employed machine learning techniques for time-series classification to investigate its acoustic properties. Our analysis reveals that a synergy between pitch and vowel quality is crucial for tone distinction, as the model trained with these features achieved the highest accuracy.
%U https://aclanthology.org/2024.fieldmatters-1.5
%P 37-42
Markdown (Informal)
[Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen](https://aclanthology.org/2024.fieldmatters-1.5) (Maspong et al., FieldMatters-WS 2024)
ACL