Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning
Rais Vaza Man Tazakka, Dessi Lestari, Ayu Purwarianti, Dipta Tanaya, Kurniawati Azizah, Sakriani Sakti
Abstract
Indonesia is home to a diverse linguistic landscape, where individuals seamlessly transition between Indonesian, English, and local dialects in their everyday conversations—a phenomenon known as code-switching. Understanding and accommodating this linguistic fluidity is essential, particularly in the development of accurate speech recognition systems. However, tackling code-switching in Indonesian poses a challenge due to the scarcity of paired code-switching data. Thus, this study endeavors to address Indonesian-English code-switching in speech recognition, leveraging unlabeled data and employing a semi-supervised technique known as the machine speech chain. Our findings demonstrate that the machine speech chain method effectively enhances Automatic Speech Recognition (ASR) performance in recognizing code-switching between Indonesian and English, utilizing previously untapped resources of unlabeled data.- Anthology ID:
- 2024.sigul-1.18
- Volume:
- Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Maite Melero, Sakriani Sakti, Claudia Soria
- Venues:
- SIGUL | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 143–148
- Language:
- URL:
- https://aclanthology.org/2024.sigul-1.18
- DOI:
- Cite (ACL):
- Rais Vaza Man Tazakka, Dessi Lestari, Ayu Purwarianti, Dipta Tanaya, Kurniawati Azizah, and Sakriani Sakti. 2024. Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning. In Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024, pages 143–148, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning (Tazakka et al., SIGUL-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.sigul-1.18.pdf