Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges

Yaqi Chen, Hao Zhang, Xukui Yang, Wenlin Zhang, Dan Qu

Abstract
Self-supervised models have demonstrated remarkable performance in speech processing by learning latent representations from large amounts of unlabeled data. Although these models yield promising results on low-resource languages, the computational expense of fine-tuning all model parameters is prohibitively high. Adapters offer a solution by incorporating lightweight bottleneck structures into pre-trained models, enabling efficient parameter adaptation for downstream tasks. However, randomly initialized adapters often underperform in low-resource scenarios, limiting their applicability in low-resource languages. To address this issue, we develop the Meta-Adapter for self-supervised models to obtain meta-initialized parameters that facilitate quick adaptation to low-resource languages. Extensive experiments on the Common Voice and FLEURS datasets demonstrate the superior performance of Meta-Adapters on 12 low-resource languages spanning four different language families. Moreover, Meta-adapters show better generalization and extensibility than traditional pretraining methods.
Anthology ID:
2024.lrec-main.979
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11215–11221
Language:
URL:
https://aclanthology.org/2024.lrec-main.979
DOI:
Bibkey:
Cite (ACL):
Yaqi Chen, Hao Zhang, Xukui Yang, Wenlin Zhang, and Dan Qu. 2024. Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11215–11221, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges (Chen et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.979.pdf
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