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arXiv · 2502.11572

Improving Rare-Word Recognition of Whisper in Zero-Shot Settings

Yash Jogi·Vaibhav Aggarwal·Shabari S Nair·Yash Verma·Aayush Kubba
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Abstract

Whisper, despite being trained on 680K hours of web-scaled audio data, faces difficulty in recognising rare words like domain-specific terms, with a solution being contextual biasing through prompting. To improve upon this method, in this paper, we propose a supervised learning strategy to fine-tune Whisper for contextual biasing instruction. We demonstrate that by using only 670 hours of Common Voice English set for fine-tuning, our model generalises to 11 diverse open-source English datasets, achieving a 45.6% improvement in recognition of rare words and 60.8% improvement in recognition of words unseen during fine-tuning over the baseline method. Surprisingly, our model's contextual biasing ability generalises even to languages unseen during fine-tuning.

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Yash Jogi, Vaibhav Aggarwal, Shabari S Nair, Yash Verma, Aayush Kubba. 2025-02-17. Improving Rare-Word Recognition of Whisper in Zero-Shot Settings. https://arxiv.org/abs/2502.11572

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