@inproceedings{lag-etal-2026-stage,
    title = "Stage-Aware Cross-Lingual Transfer for {F}aroese {ASR}: When and Which Languages Matter",
    author = "L{\'a}g, D{\'a}vid {\'i}  and
      Scalvini, Barbara  and
      Mena, Carlos Daniel  and
      Gu{\dh}nason, J{\'o}n",
    editor = "Hosseini-Kivanani, Nina  and
      Brutti, Alessio  and
      Matassoni, Marco  and
      Dowerah, Sandipana  and
      Liga, Davide  and
      Schommer, Christoph",
    booktitle = "Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis ({SPEAKABLE}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-previews/2026.speakable-1.17/",
    doi = "10.63317/5hpzgtgaxf43",
    pages = "150--161",
    abstract = "Automatic speech recognition (ASR) for low-resource languages remains challenging due to limited labeled data. Although multilingual models and the inclusion of related auxiliary languages enable cross-lingual transfer, it is still unclear how introducing cross-lingual information at different training stages-pre-training versus fine-tuning-affects downstream performance. Prior work largely treats transfer as a single-stage optimization problem without disentangling stage effects. We present a stage-aware analysis of cross-lingual transfer for Faroese ASR using related auxiliary languages and Wav2Vec 2.0 XLS-R models. We systematically compare two complementary adaptation pipelines: (i) cross-lingual supervised fine-tuning and (ii) cross-lingual continuous pre-training prior to fine-tuning. Both strategies are evaluated under a unified setup with controlled model architectures, balanced representation of auxiliary languages, and identical evaluation protocols. Results demonstrate that cross-lingual transfer is stage-dependent. Supervised adaptation optimizes in-domain accuracy, while pretraining-level adaptation enhances robustness and reduces Character Error Rate (CER). Auxiliary language effects vary across pipelines, reinforcing the idea that transfer effectiveness depends on when and how cross-lingual information is introduced. Comparisons with large-scale multilingual ASR models highlight trade-offs between model scale and explicit, small-scale domain-aware adaptation. These findings suggest that effective cross-lingual transfer for Faroese low-resource ASR is inherently stage-dependent rather than a single-step design choice."
}