@inproceedings{tazakka-etal-2024-indonesian,
title = "{I}ndonesian-{E}nglish Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning",
author = "Tazakka, Rais Vaza Man and
Lestari, Dessi and
Purwarianti, Ayu and
Tanaya, Dipta and
Azizah, Kurniawati and
Sakti, Sakriani",
editor = "Melero, Maite and
Sakti, Sakriani and
Soria, Claudia",
booktitle = "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",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.sigul-1.18",
pages = "143--148",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning
%A Tazakka, Rais Vaza Man
%A Lestari, Dessi
%A Purwarianti, Ayu
%A Tanaya, Dipta
%A Azizah, Kurniawati
%A Sakti, Sakriani
%Y Melero, Maite
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tazakka-etal-2024-indonesian
%X 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.
%U https://aclanthology.org/2024.sigul-1.18
%P 143-148
Markdown (Informal)
[Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning](https://aclanthology.org/2024.sigul-1.18) (Tazakka et al., SIGUL-WS 2024)
ACL