Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech

Katrin Tomanek, Vicky Zayats, Dirk Padfield, Kara Vaillancourt, Fadi Biadsy


Abstract
Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech. It has previously been shown that personalization through model fine-tuning substantially improves performance. However, maintaining such large models per speaker is costly and difficult to scale. We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to model fine-tuning, while only updating a tiny fraction (less than 0.5%) of the model parameters. We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.
Anthology ID:
2021.emnlp-main.541
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6751–6760
Language:
URL:
https://aclanthology.org/2021.emnlp-main.541
DOI:
10.18653/v1/2021.emnlp-main.541
Bibkey:
Cite (ACL):
Katrin Tomanek, Vicky Zayats, Dirk Padfield, Kara Vaillancourt, and Fadi Biadsy. 2021. Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6751–6760, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech (Tomanek et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.541.pdf
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 https://aclanthology.org/2021.emnlp-main.541.mp4