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原文摘要

Low-resource automatic speech recognition (ASR) often relies on cross-lingual transfer from higher-resource languages due to limited availability of transcribed speech. However, selecting an effective donor language is a nontrivial issue, particularly in settings with spontaneous speech, linguistic variation, and uneven resource availability. We present DonorRank, a learning-to-rank framework that uses linguistic and dataset-level features along with previously observed transfer outcomes to predict promising donor languages for zero-shot ASR. We evaluate DonorRank on two multilingual speech corpora of Indic and African languages. DonorRank produces informative donor rankings across both collections, outperforming common heuristics in the more closely related Indic setting while remaining competitive in the more linguistically diverse African setting. Our analysis reveals that effective donors are not always the closest linguistic relatives and that the most informative predictors vary across language collections. These findings highlight the value of learned donor selection for prioritizing transfer experiments in low-resource ASR. We will also release all of our evaluation results, feature matrices for all languages, scripts for training the DonorRank framework, and pairwise transfer performance matrices for our datasets.