@inproceedings{zhang-etal-2024-evaluation,
    title = "An Evaluation of {C}roatian {ASR} Models for {\v{C}}akavian Transcription",
    author = "Zhang, Shulin  and
      Hale, John  and
      Renwick, Margaret  and
      Vrzi{\'c}, Zvjezdana  and
      Langston, Keith",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.98",
    pages = "1098--1104",
    abstract = "To assist in the documentation of {\v{C}}akavian, an endangered language variety closely related to Croatian, we test four currently available ASR models that are trained with Croatian data and assess their performance in the transcription of {\v{C}}akavian audio data. We compare the models{'} word error rates, analyze the word-level error types, and showcase the most frequent Deletion and Substitution errors. The evaluation results indicate that the best-performing system for transcribing {\v{C}}akavian was a CTC-based variant of the Conformer model.",
}

To assist in the documentation of {\v{C}}akavian, an endangered language variety closely related to Croatian, we test four currently available ASR models that are trained with Croatian data and assess their performance in the transcription of {\v{C}}akavian audio data. We compare the models{'} word error rates, analyze the word-level error types, and showcase the most frequent Deletion and Substitution errors. The evaluation results indicate that the best-performing system for transcribing {\v{C}}akavian was a CTC-based variant of the Conformer model.
Zhang, Shulin Hale, John Renwick, Margaret Vrzi{\'c}, Zvjezdana Langston, Keith
An Evaluation of Croatian ASR Models for Čakavian Transcription