@inproceedings{hopton-chodroff-2025-impact,
    title = "The Impact of Dialect Variation on Robust Automatic Speech Recognition for {C}atalan",
    author = "Hopton, Zachary  and
      Chodroff, Eleanor",
    editor = {Nicolai, Garrett  and
      Chodroff, Eleanor  and
      Mailhot, Frederic  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the The 22nd SIGMORPHON workshop on Computational Morphology, Phonology, and Phonetics",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sigmorphon-main.3/",
    pages = "23--33",
    ISBN = "979-8-89176-231-2",
    abstract = {To accurately transcribe a speech signal, automatic speech recognition (ASR) systems must show robustness to a wide range of task independent variation, such as speaker factors, recording quality, or even {\"a}dversarial noisedesigned to disrupt performance.We manipulated the dialect composition of fine-tuning data for ASR to study whether balancing the relative proportion of dialects had an impact on models robustness to two such sources of variation{\textquotedblright}:'' dialect variation and adversarial perturbations. We fine-tuned XLSR-53 for Catalan ASR using four different dialect compositions, each containing the Central Catalan dialect. These were defined as 100{\%}, 80{\%}, 50{\%}, and 20{\%} Central Catalan, with the remaining portions split evenly between four other Catalan dialects. While increasing the relative proportion of dialect variants improved models' dialect robustness, this did not have a meaningful impact on adversarial robustness. These findings suggest that while improvements to ASR can be made by diversifying the training data, such changes do not sufficiently counteract adversarial attacks, leaving the technology open to security threats.}
}