Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR

Abstract:Extending large-scale multilingual automatic speech recognition (ASR) models to low-resource languages remains challenging. Model performance is skewed toward high-resource languages and degrades sharply for languages with limited labeled data and pre-training exposure. To address this, we investigate parameter-efficient approaches for transferring knowledge from resource-rich source languages to low-resource target languages on Whisper. Alongside warm initialization and attention-based fusion, we propose Sequential Adapter Stacking, which places a trainable target-language adapter on top of a frozen source-language adapter. Under controlled experiments, these approaches are evaluated on three target languages unsupported by Whisper -- Asturian, Assamese, and Xhosa -- using source languages with varying degrees of relatedness. Sequential Adapter Stacking with the closest related source consistently and significantly outperforms full fine-tuning across the three targets, with 5--8\% relative WER reductions. These gains largely persist with only one hour of target training data.

Computation and Language (cs.CL)

arXiv:2609.15758 [cs.CL]

(or arXiv:2609.15758v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.15758 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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