DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation

Suraj Kothawade, Anmol Mekala, D.Chandra Sekhara Hetha Havya, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi


Abstract
State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent, a commonly adopted solution is to finetune the ASR model using accent-specific labeled speech. However, acquiring large amounts of labeled speech for specific target accents is challenging. Choosing an informative subset of speech samples that are most representative of the target accents becomes important for effective ASR finetuning. To address this problem, we propose DITTO (Data-efficient and faIr Targeted subseT selectiOn that uses Submodular Mutual Information (SMI) functions as acquisition functions to find the most informative set of utterances matching a target accent within a fixed budget. An important feature of DITTO is that it supports fair targeting for multiple accents, i.e. it can automatically select representative data points from multiple accents when the ASR model needs to perform well on more than one accent. We show that compared to other speech selection methods, DITTO is 3-5 times as label-efficient for its improvements on the Indic-TTS and L2 datasets.
Anthology ID:
2023.acl-long.319
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5810–5822
Language:
URL:
https://aclanthology.org/2023.acl-long.319
DOI:
10.18653/v1/2023.acl-long.319
Bibkey:
Cite (ACL):
Suraj Kothawade, Anmol Mekala, D.Chandra Sekhara Hetha Havya, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, and Preethi Jyothi. 2023. DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5810–5822, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation (Kothawade et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-long.319.pdf
Video:
 https://aclanthology.org/2023.acl-long.319.mp4