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Stage-Aware Cross-Lingual Transfer for Faroese ASR: When and Which Languages Matter

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.