COLING 2024main0 citations
An Evaluation of Croatian ASR Models for Čakavian Transcription
Shulin Zhang, John Hale, Margaret Renwick, Zvjezdana Vrzić, Keith Langston
Abstract
To assist in the documentation of Čakavian, 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 Čakavian 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 Čakavian was a CTC-based variant of the Conformer model.
BibTeX
@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"
}