Evaluating the Efficacy of Large Acoustic Model for Documenting Non-Orthographic Tribal Languages in India
Tonmoy Rajkhowa, Amartya Roy Chowdhury, Hrishikesh Ravindra Karande, S. R. Mahadeva Prasanna
Abstract
Pre-trained Large Acoustic Models, when fine-tuned, have largely shown to improve the performances in various tasks related to spoken language technologies. However, their evaluation has been mostly on datasets that contain English or other widely spoken languages, and their potential for novel under-resourced languages is not fully known. In this work, four novel under-resourced tribal languages that do not have a standard writing system were introduced and the application of such large pre-trained models was assessed to document such languages using Automatic Speech Recognition and Direct Speech-to-Text Translation systems. The transcriptions for these tribal languages were generated by adapting scripts from those languages that held a prominent presence in the geographical regions where these tribal languages are spoken. The results from this study suggest a viable direction to document these languages in the electronic domain by using Spoken Language Technologies that incorporate LAMs. Additionally, this study helped in understanding the varying performances exhibited by the Large Acoustic Model between these four languages. This study not only informs the adoption of appropriate scripts for transliterating spoken-only languages based on the language family but also aids in making informed decisions in analyzing the behavior of particular Large Acoustic Model in linguistic contexts.- Anthology ID:
- 2024.lrec-main.574
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 6475–6483
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.574
- DOI:
- Cite (ACL):
- Tonmoy Rajkhowa, Amartya Roy Chowdhury, Hrishikesh Ravindra Karande, and S. R. Mahadeva Prasanna. 2024. Evaluating the Efficacy of Large Acoustic Model for Documenting Non-Orthographic Tribal Languages in India. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6475–6483, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Evaluating the Efficacy of Large Acoustic Model for Documenting Non-Orthographic Tribal Languages in India (Rajkhowa et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.574.pdf