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arXiv · 2311.08402

Retrieve and Copy: Scaling ASR Personalization to Large Catalogs

Sai Muralidhar Jayanthi·Devang Kulshreshtha·Saket Dingliwal·Srikanth Ronanki·Sravan Bodapati
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Abstract

Personalization of automatic speech recognition (ASR) models is a widely studied topic because of its many practical applications. Most recently, attention-based contextual biasing techniques are used to improve the recognition of rare words and domain specific entities. However, due to performance constraints, the biasing is often limited to a few thousand entities, restricting real-world usability. To address this, we first propose a "Retrieve and Copy" mechanism to improve latency while retaining the accuracy even when scaled to a large catalog. We also propose a training strategy to overcome the degradation in recall at such scale due to an increased number of confusing entities. Overall, our approach achieves up to 6% more Word Error Rate reduction (WERR) and 3.6% absolute improvement in F1 when compared to a strong baseline. Our method also allows for large catalog sizes of up to 20K without significantly affecting WER and F1-scores, while achieving at least 20% inference speedup per acoustic frame.

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Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal, Srikanth Ronanki, Sravan Bodapati. 2023-11-14. Retrieve and Copy: Scaling ASR Personalization to Large Catalogs. https://arxiv.org/abs/2311.08402

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