Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation

Nivedita Sethiya, Saanvi Nair, Chandresh Maurya


Abstract
Speech-to-text (ST) task is the translation of speech in a language to text in a different language. It has use cases in subtitling, dubbing, etc. Traditionally, ST task has been solved by cascading automatic speech recognition (ASR) and machine translation (MT) models which leads to error propagation, high latency, and training time. To minimize such issues, end-to-end models have been proposed recently. However, we find that only a few works have reported results of ST models on a limited number of low-resource languages. To take a step further in this direction, we release datasets and baselines for low-resource ST tasks. Concretely, our dataset has 9 language pairs and benchmarking has been done against SOTA ST models. The low performance of SOTA ST models on Indic-TEDST data indicates the necessity of the development of ST models specifically designed for low-resource languages.
Anthology ID:
2024.lrec-main.790
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:
9019–9024
Language:
URL:
https://aclanthology.org/2024.lrec-main.790
DOI:
Bibkey:
Cite (ACL):
Nivedita Sethiya, Saanvi Nair, and Chandresh Maurya. 2024. Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9019–9024, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation (Sethiya et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.790.pdf