Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen
Sireemas Maspong, Francesco Burroni, Teerawee Sukanchanon, Warunsiri Pornpottanamas, Pittayawat Pittayaporn
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.- Anthology ID:
- 2024.fieldmatters-1.5
- Volume:
- Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Oleg Serikov, Ekaterina Voloshina, Anna Postnikova, Saliha Muradoglu, Eric Le Ferrand, Elena Klyachko, Ekaterina Vylomova, Tatiana Shavrina, Francis Tyers
- Venues:
- FieldMatters | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 37–42
- Language:
- URL:
- https://aclanthology.org/2024.fieldmatters-1.5
- DOI:
- 10.18653/v1/2024.fieldmatters-1.5
- Cite (ACL):
- Sireemas Maspong, Francesco Burroni, Teerawee Sukanchanon, Warunsiri Pornpottanamas, and Pittayawat Pittayaporn. 2024. Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen. In Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024), pages 37–42, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen (Maspong et al., FieldMatters-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.fieldmatters-1.5.pdf