Task Arithmetic with Support Languages for Low-Resource ASR

• URL: http://arxiv.org/abs/2601.07038v1
• Date: Sun, 11 Jan 2026 19:24:05 GMT
• Title: Task Arithmetic with Support Languages for Low-Resource ASR
• Authors: Emma Rafkin, Dan DeGenaro, Xiulin Yang,
• Abstract summary: Existing approaches to many low-resource natural language processing tasks leverage additional data from higher-resource languages.<n>One increasingly popular approach uses task arithmetic to combine models trained on different tasks to create a model for a task where there is little to no training data.<n>In this paper, we consider training on a particular language to be a task, and we generate task vectors by fine-tuning variants of the Whisper ASR system.
• Score: 2.0368746869445236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of resource-constrained approaches to automatic speech recognition (ASR) is of great interest due to its broad applicability to many low-resource languages for which there is scant usable data. Existing approaches to many low-resource natural language processing tasks leverage additional data from higher-resource languages that are closely related to a target low-resource language. One increasingly popular approach uses task arithmetic to combine models trained on different tasks to create a model for a task where there is little to no training data. In this paper, we consider training on a particular language to be a task, and we generate task vectors by fine-tuning variants of the Whisper ASR system. For pairings of high- and low-resource languages, we merge task vectors via a linear combination, optimizing the weights of the linear combination on the downstream word error rate on the low-resource target language's validation set. We find that this approach consistently improves performance on the target languages.

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