Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR

• URL: http://arxiv.org/abs/2609.15758v1
• Date: Mon, 14 Sep 2026 15:43:41 GMT
• Title: Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR
• Abstract summary: We investigate approaches for transferring knowledge from source languages to low-resource target languages on Whisper.<n>We propose Sequential Adapter Stacking, which places a trainable target-language adapter on top of a frozen source-language adapter.<n>These approaches are evaluated on three target languages unsupported by Whisper.
• Score: 2.27726424067057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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