@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.",
}

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.
Tazakka, Rais Vaza Man Lestari, Dessi Purwarianti, Ayu Tanaya, Dipta Azizah, Kurniawati Sakti, Sakriani
Indonesian-English Code-Switching Speech Recognition Using the Machine Speech Chain Based Semi-Supervised Learning