Retrieve and Copy: Scaling ASR Personalization to Large Catalogs

Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal, Srikanth Ronanki, Sravan Bodapati


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/or 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.
Anthology ID:
2023.emnlp-industry.60
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2023
Address:
Singapore
Editors:
Mingxuan Wang, Imed Zitouni
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
631–639
Language:
URL:
https://aclanthology.org/2023.emnlp-industry.60
DOI:
10.18653/v1/2023.emnlp-industry.60
Bibkey:
Cite (ACL):
Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal, Srikanth Ronanki, and Sravan Bodapati. 2023. Retrieve and Copy: Scaling ASR Personalization to Large Catalogs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 631–639, Singapore. Association for Computational Linguistics.
Cite (Informal):
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs (Jayanthi et al., EMNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.emnlp-industry.60.pdf
Video:
 https://aclanthology.org/2023.emnlp-industry.60.mp4