@article{fedchenko-jordan-2026-leveraging,
    title = "Leveraging Linguistic Similarity for Low-Resource Speech Transcription",
    author = "Fedchenko, Valentina  and
      Jordan, Eric",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.288/",
    pages = "3590--3598",
    abstract = "This study investigates how large-scale, self-supervised acoustic models (like XLSR and MMS) represent linguistic similarity and whether this can optimize Automatic Speech Recognition (ASR) for low-resource and dialectally diverse languages. While these models excel at cross-lingual transfer learning, their internal representations of fine-grained dialectal variation remain opaque. We focus on Yiddish, a language with a complex dialect continuum, to test if a model{'}s internal acoustic similarity metric{---}Acoustic Token Distribution Similarity (ATDS){---}predicts ASR performance. Our methodology involved fine-tuning models on Yiddish dialects and measuring ATDS between Yiddish and related languages. Results confirm that ATDS is a meaningful predictor: higher acoustic similarity in the model{'}s latent space correlates with lower character error rates (CER) after fine-tuning. This relationship is strongest in mid-to-upper layers of the MMS model and for in-domain data. Crucially, ATDS captures model-dependent acoustic similarity, which does not always align with genealogical linguistic relationships but remains a practical indicator of transfer learning potential. We conclude that ATDS is a valuable tool for selecting donor languages to develop more efficient, dialect-sensitive ASR systems for language documentation, even if its absolute values require careful interpretation against linguistic knowledge."
}